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models/model_1/Llama-2-7b-hf/LICENSE.txt ADDED
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+ LLAMA 2 COMMUNITY LICENSE AGREEMENT
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+ Llama 2 Version Release Date: July 18, 2023
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+
models/model_1/Llama-2-7b-hf/README.md ADDED
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1
+ ---
2
+ extra_gated_heading: Access Llama 2 on Hugging Face
3
+ extra_gated_description: >-
4
+ This is a form to enable access to Llama 2 on Hugging Face after you have been
5
+ granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
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+ license terms and acceptable use policy before submitting this form. Requests
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+ will be processed in 1-2 days.
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+ extra_gated_button_content: Submit
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+ extra_gated_fields:
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+ I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
11
+ language:
12
+ - en
13
+ pipeline_tag: text-generation
14
+ inference: false
15
+ tags:
16
+ - facebook
17
+ - meta
18
+ - pytorch
19
+ - llama
20
+ - llama-2
21
+ ---
22
+ # **Llama 2**
23
+ Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
24
+
25
+ ## Model Details
26
+ *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
27
+
28
+ Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
29
+
30
+ **Model Developers** Meta
31
+
32
+ **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
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+
34
+ **Input** Models input text only.
35
+
36
+ **Output** Models generate text only.
37
+
38
+ **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
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+
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+
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+ ||Training Data|Params|Content Length|GQA|Tokens|LR|
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+ |---|---|---|---|---|---|---|
43
+ |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
46
+
47
+ *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
48
+
49
+ **Model Dates** Llama 2 was trained between January 2023 and July 2023.
50
+
51
+ **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
52
+
53
+ **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
54
+
55
+ ## Intended Use
56
+ **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
57
+
58
+ To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
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+
60
+ **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
61
+
62
+ ## Hardware and Software
63
+ **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
64
+
65
+ **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
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+
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+ ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
68
+ |---|---|---|---|
69
+ |Llama 2 7B|184320|400|31.22|
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+ |Llama 2 13B|368640|400|62.44|
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+ |Llama 2 70B|1720320|400|291.42|
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+ |Total|3311616||539.00|
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+
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+ **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
75
+
76
+ ## Training Data
77
+ **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
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+
79
+ **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
80
+
81
+ ## Evaluation Results
82
+
83
+ In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
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+
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+ |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
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+ |---|---|---|---|---|---|---|---|---|---|
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+ |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
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+ |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
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+ |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
90
+ |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
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+ |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
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+ |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
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+ |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
94
+
95
+ **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
96
+
97
+ |||TruthfulQA|Toxigen|
98
+ |---|---|---|---|
99
+ |Llama 1|7B|27.42|23.00|
100
+ |Llama 1|13B|41.74|23.08|
101
+ |Llama 1|33B|44.19|22.57|
102
+ |Llama 1|65B|48.71|21.77|
103
+ |Llama 2|7B|33.29|**21.25**|
104
+ |Llama 2|13B|41.86|26.10|
105
+ |Llama 2|70B|**50.18**|24.60|
106
+
107
+ **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
108
+
109
+
110
+ |||TruthfulQA|Toxigen|
111
+ |---|---|---|---|
112
+ |Llama-2-Chat|7B|57.04|**0.00**|
113
+ |Llama-2-Chat|13B|62.18|**0.00**|
114
+ |Llama-2-Chat|70B|**64.14**|0.01|
115
+
116
+ **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
117
+
118
+ ## Ethical Considerations and Limitations
119
+ Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
120
+
121
+ Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
122
+
123
+ ## Reporting Issues
124
+ Please report any software “bug,” or other problems with the models through one of the following means:
125
+ - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
126
+ - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
127
+ - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
128
+
129
+ ## Llama Model Index
130
+ |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
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+ |---|---|---|---|---|
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+ |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
133
+ |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
134
+ |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
models/model_1/Llama-2-7b-hf/Responsible-Use-Guide.pdf ADDED
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models/model_1/Llama-2-7b-hf/USE_POLICY.md ADDED
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1
+ # Llama 2 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
4
+
5
+ ## Prohibited Uses
6
+ We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
7
+
8
+ 1. Violate the law or others’ rights, including to:
9
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
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+ 1. Violence or terrorism
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+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
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+ 3. Human trafficking, exploitation, and sexual violence
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+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
14
+ 5. Sexual solicitation
15
+ 6. Any other criminal activity
16
+ 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
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+ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
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+ 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
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+ 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
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+ 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
21
+ 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
22
+
23
+
24
+
25
+ 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
26
+ 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
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+ 2. Guns and illegal weapons (including weapon development)
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+ 3. Illegal drugs and regulated/controlled substances
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+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
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+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
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+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
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+
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+
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+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
36
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
37
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
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+ 3. Generating, promoting, or further distributing spam
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+ 4. Impersonating another individual without consent, authorization, or legal right
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+ 5. Representing that the use of Llama 2 or outputs are human-generated
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+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
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+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
43
+
44
+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
45
+
46
+ * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
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+ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
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+ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
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+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])
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models/model_4/Llama-2-7b-hf/README.md ADDED
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+ ---
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+ extra_gated_heading: Access Llama 2 on Hugging Face
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+ extra_gated_description: >-
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+ This is a form to enable access to Llama 2 on Hugging Face after you have been
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+ granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
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+ license terms and acceptable use policy before submitting this form. Requests
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+ will be processed in 1-2 days.
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+ extra_gated_button_content: Submit
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+ extra_gated_fields:
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+ I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ inference: false
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+ tags:
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+ - facebook
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+ - meta
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+ - pytorch
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+ - llama
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+ - llama-2
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+ ---
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+ # **Llama 2**
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+ Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
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+
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+ ## Model Details
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+ *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
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+
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+ Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
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+
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+ **Model Developers** Meta
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+
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+ **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
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+
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+ **Input** Models input text only.
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+
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+ **Output** Models generate text only.
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+
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+ **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
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+
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+
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+ ||Training Data|Params|Content Length|GQA|Tokens|LR|
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+ |---|---|---|---|---|---|---|
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+ |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
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+
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+ *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
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+
49
+ **Model Dates** Llama 2 was trained between January 2023 and July 2023.
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+
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+ **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
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+
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+ **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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+
55
+ ## Intended Use
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+ **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
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+
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+ To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
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+
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+ **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
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+
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+ ## Hardware and Software
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+ **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
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+
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+ **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
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+
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+ ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
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+ |---|---|---|---|
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+ |Llama 2 7B|184320|400|31.22|
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+ |Llama 2 13B|368640|400|62.44|
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+ |Llama 2 70B|1720320|400|291.42|
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+ |Total|3311616||539.00|
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+
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+ **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
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+
76
+ ## Training Data
77
+ **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
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+
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+ **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
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+
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+ ## Evaluation Results
82
+
83
+ In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
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+
85
+ |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
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+ |---|---|---|---|---|---|---|---|---|---|
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+ |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
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+ |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
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+ |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
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+ |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
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+ |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
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+ |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
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+ |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
94
+
95
+ **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
96
+
97
+ |||TruthfulQA|Toxigen|
98
+ |---|---|---|---|
99
+ |Llama 1|7B|27.42|23.00|
100
+ |Llama 1|13B|41.74|23.08|
101
+ |Llama 1|33B|44.19|22.57|
102
+ |Llama 1|65B|48.71|21.77|
103
+ |Llama 2|7B|33.29|**21.25**|
104
+ |Llama 2|13B|41.86|26.10|
105
+ |Llama 2|70B|**50.18**|24.60|
106
+
107
+ **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
108
+
109
+
110
+ |||TruthfulQA|Toxigen|
111
+ |---|---|---|---|
112
+ |Llama-2-Chat|7B|57.04|**0.00**|
113
+ |Llama-2-Chat|13B|62.18|**0.00**|
114
+ |Llama-2-Chat|70B|**64.14**|0.01|
115
+
116
+ **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
117
+
118
+ ## Ethical Considerations and Limitations
119
+ Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
120
+
121
+ Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
122
+
123
+ ## Reporting Issues
124
+ Please report any software “bug,” or other problems with the models through one of the following means:
125
+ - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
126
+ - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
127
+ - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
128
+
129
+ ## Llama Model Index
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+ |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
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+ |---|---|---|---|---|
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+ |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
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+ |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
134
+ |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
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1
+ # Llama 2 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
4
+
5
+ ## Prohibited Uses
6
+ We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
7
+
8
+ 1. Violate the law or others’ rights, including to:
9
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
10
+ 1. Violence or terrorism
11
+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
12
+ 3. Human trafficking, exploitation, and sexual violence
13
+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
14
+ 5. Sexual solicitation
15
+ 6. Any other criminal activity
16
+ 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
17
+ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
18
+ 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
19
+ 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
20
+ 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
21
+ 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
22
+
23
+
24
+
25
+ 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
26
+ 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
27
+ 2. Guns and illegal weapons (including weapon development)
28
+ 3. Illegal drugs and regulated/controlled substances
29
+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
30
+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
31
+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
32
+
33
+
34
+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
36
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
37
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
38
+ 3. Generating, promoting, or further distributing spam
39
+ 4. Impersonating another individual without consent, authorization, or legal right
40
+ 5. Representing that the use of Llama 2 or outputs are human-generated
41
+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
42
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
43
+
44
+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
45
+
46
+ * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
47
+ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
48
+ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
49
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])
50
+
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+ ---
2
+ library_name: peft
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+ base_model: /home/asrix01/Downloads/models/Llama-2-7b-hf
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+ ---
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+ ## Training procedure
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+
7
+
8
+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
151
+ The following `bitsandbytes` quantization config was used during training:
152
+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
163
+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
168
+ - llm_int8_has_fp16_weight: False
169
+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
173
+ The following `bitsandbytes` quantization config was used during training:
174
+ - load_in_8bit: False
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+ - load_in_4bit: True
176
+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
184
+ The following `bitsandbytes` quantization config was used during training:
185
+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
206
+ The following `bitsandbytes` quantization config was used during training:
207
+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
215
+ - bnb_4bit_compute_dtype: float16
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+
217
+ The following `bitsandbytes` quantization config was used during training:
218
+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
228
+ The following `bitsandbytes` quantization config was used during training:
229
+ - load_in_8bit: False
230
+ - load_in_4bit: True
231
+ - llm_int8_threshold: 6.0
232
+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
234
+ - llm_int8_has_fp16_weight: False
235
+ - bnb_4bit_quant_type: fp4
236
+ - bnb_4bit_use_double_quant: False
237
+ - bnb_4bit_compute_dtype: float16
238
+
239
+ The following `bitsandbytes` quantization config was used during training:
240
+ - load_in_8bit: False
241
+ - load_in_4bit: True
242
+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
244
+ - llm_int8_enable_fp32_cpu_offload: False
245
+ - llm_int8_has_fp16_weight: False
246
+ - bnb_4bit_quant_type: fp4
247
+ - bnb_4bit_use_double_quant: False
248
+ - bnb_4bit_compute_dtype: float16
249
+
250
+ The following `bitsandbytes` quantization config was used during training:
251
+ - load_in_8bit: False
252
+ - load_in_4bit: True
253
+ - llm_int8_threshold: 6.0
254
+ - llm_int8_skip_modules: None
255
+ - llm_int8_enable_fp32_cpu_offload: False
256
+ - llm_int8_has_fp16_weight: False
257
+ - bnb_4bit_quant_type: fp4
258
+ - bnb_4bit_use_double_quant: False
259
+ - bnb_4bit_compute_dtype: float16
260
+
261
+ The following `bitsandbytes` quantization config was used during training:
262
+ - load_in_8bit: False
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+ - load_in_4bit: True
264
+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - bnb_4bit_quant_type: fp4
269
+ - bnb_4bit_use_double_quant: False
270
+ - bnb_4bit_compute_dtype: float16
271
+
272
+ The following `bitsandbytes` quantization config was used during training:
273
+ - load_in_8bit: False
274
+ - load_in_4bit: True
275
+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
278
+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float16
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+
283
+ The following `bitsandbytes` quantization config was used during training:
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+ - load_in_8bit: False
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+ - load_in_4bit: True
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
292
+ - bnb_4bit_compute_dtype: float16
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+
294
+ The following `bitsandbytes` quantization config was used during training:
295
+ - load_in_8bit: False
296
+ - load_in_4bit: True
297
+ - llm_int8_threshold: 6.0
298
+ - llm_int8_skip_modules: None
299
+ - llm_int8_enable_fp32_cpu_offload: False
300
+ - llm_int8_has_fp16_weight: False
301
+ - bnb_4bit_quant_type: fp4
302
+ - bnb_4bit_use_double_quant: False
303
+ - bnb_4bit_compute_dtype: float16
304
+
305
+ The following `bitsandbytes` quantization config was used during training:
306
+ - load_in_8bit: False
307
+ - load_in_4bit: True
308
+ - llm_int8_threshold: 6.0
309
+ - llm_int8_skip_modules: None
310
+ - llm_int8_enable_fp32_cpu_offload: False
311
+ - llm_int8_has_fp16_weight: False
312
+ - bnb_4bit_quant_type: fp4
313
+ - bnb_4bit_use_double_quant: False
314
+ - bnb_4bit_compute_dtype: float16
315
+
316
+ The following `bitsandbytes` quantization config was used during training:
317
+ - load_in_8bit: False
318
+ - load_in_4bit: True
319
+ - llm_int8_threshold: 6.0
320
+ - llm_int8_skip_modules: None
321
+ - llm_int8_enable_fp32_cpu_offload: False
322
+ - llm_int8_has_fp16_weight: False
323
+ - bnb_4bit_quant_type: nf4
324
+ - bnb_4bit_use_double_quant: False
325
+ - bnb_4bit_compute_dtype: float16
326
+
327
+ The following `bitsandbytes` quantization config was used during training:
328
+ - load_in_8bit: False
329
+ - load_in_4bit: True
330
+ - llm_int8_threshold: 6.0
331
+ - llm_int8_skip_modules: None
332
+ - llm_int8_enable_fp32_cpu_offload: False
333
+ - llm_int8_has_fp16_weight: False
334
+ - bnb_4bit_quant_type: nf4
335
+ - bnb_4bit_use_double_quant: False
336
+ - bnb_4bit_compute_dtype: float16
337
+
338
+ The following `bitsandbytes` quantization config was used during training:
339
+ - load_in_8bit: False
340
+ - load_in_4bit: True
341
+ - llm_int8_threshold: 6.0
342
+ - llm_int8_skip_modules: None
343
+ - llm_int8_enable_fp32_cpu_offload: False
344
+ - llm_int8_has_fp16_weight: False
345
+ - bnb_4bit_quant_type: nf4
346
+ - bnb_4bit_use_double_quant: False
347
+ - bnb_4bit_compute_dtype: float16
348
+
349
+ The following `bitsandbytes` quantization config was used during training:
350
+ - load_in_8bit: False
351
+ - load_in_4bit: True
352
+ - llm_int8_threshold: 6.0
353
+ - llm_int8_skip_modules: None
354
+ - llm_int8_enable_fp32_cpu_offload: False
355
+ - llm_int8_has_fp16_weight: False
356
+ - bnb_4bit_quant_type: nf4
357
+ - bnb_4bit_use_double_quant: False
358
+ - bnb_4bit_compute_dtype: float16
359
+
360
+ The following `bitsandbytes` quantization config was used during training:
361
+ - load_in_8bit: False
362
+ - load_in_4bit: True
363
+ - llm_int8_threshold: 6.0
364
+ - llm_int8_skip_modules: None
365
+ - llm_int8_enable_fp32_cpu_offload: False
366
+ - llm_int8_has_fp16_weight: False
367
+ - bnb_4bit_quant_type: nf4
368
+ - bnb_4bit_use_double_quant: False
369
+ - bnb_4bit_compute_dtype: float16
370
+
371
+ The following `bitsandbytes` quantization config was used during training:
372
+ - load_in_8bit: False
373
+ - load_in_4bit: True
374
+ - llm_int8_threshold: 6.0
375
+ - llm_int8_skip_modules: None
376
+ - llm_int8_enable_fp32_cpu_offload: False
377
+ - llm_int8_has_fp16_weight: False
378
+ - bnb_4bit_quant_type: nf4
379
+ - bnb_4bit_use_double_quant: False
380
+ - bnb_4bit_compute_dtype: float16
381
+
382
+ The following `bitsandbytes` quantization config was used during training:
383
+ - load_in_8bit: False
384
+ - load_in_4bit: True
385
+ - llm_int8_threshold: 6.0
386
+ - llm_int8_skip_modules: None
387
+ - llm_int8_enable_fp32_cpu_offload: False
388
+ - llm_int8_has_fp16_weight: False
389
+ - bnb_4bit_quant_type: nf4
390
+ - bnb_4bit_use_double_quant: False
391
+ - bnb_4bit_compute_dtype: float16
392
+
393
+ The following `bitsandbytes` quantization config was used during training:
394
+ - load_in_8bit: False
395
+ - load_in_4bit: True
396
+ - llm_int8_threshold: 6.0
397
+ - llm_int8_skip_modules: None
398
+ - llm_int8_enable_fp32_cpu_offload: False
399
+ - llm_int8_has_fp16_weight: False
400
+ - bnb_4bit_quant_type: nf4
401
+ - bnb_4bit_use_double_quant: False
402
+ - bnb_4bit_compute_dtype: float16
403
+ ### Framework versions
404
+
405
+ - PEFT 0.4.0
406
+ - PEFT 0.4.0
407
+ - PEFT 0.4.0
408
+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.7.1
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
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+ - PEFT 0.4.0
439
+ - PEFT 0.4.0
440
+ - PEFT 0.4.0
441
+
442
+ - PEFT 0.4.0
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