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--- |
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base_model: Replete-AI/Llama3-8B-Instruct-Replete-Adapted |
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datasets: |
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- Replete-AI/code_bagel_hermes-2.5 |
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- Replete-AI/code_bagel |
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- Replete-AI/OpenHermes-2.5-Uncensored |
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- teknium/OpenHermes-2.5 |
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- layoric/tiny-codes-alpaca |
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- glaiveai/glaive-code-assistant-v3 |
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- ajibawa-2023/Code-290k-ShareGPT |
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- TIGER-Lab/MathInstruct |
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- chargoddard/commitpack-ft-instruct-rated |
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- iamturun/code_instructions_120k_alpaca |
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- ise-uiuc/Magicoder-Evol-Instruct-110K |
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- cognitivecomputations/dolphin-coder |
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- nickrosh/Evol-Instruct-Code-80k-v1 |
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- coseal/CodeUltraFeedback_binarized |
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- glaiveai/glaive-function-calling-v2 |
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- CyberNative/Code_Vulnerability_Security_DPO |
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- jondurbin/airoboros-2.2 |
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- camel-ai |
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- lmsys/lmsys-chat-1m |
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- CollectiveCognition/chats-data-2023-09-22 |
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- CoT-Alpaca-GPT4 |
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- WizardLM/WizardLM_evol_instruct_70k |
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- WizardLM/WizardLM_evol_instruct_V2_196k |
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- teknium/GPT4-LLM-Cleaned |
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- GPTeacher |
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- OpenGPT |
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- meta-math/MetaMathQA |
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- Open-Orca/SlimOrca |
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- garage-bAInd/Open-Platypus |
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- anon8231489123/ShareGPT_Vicuna_unfiltered |
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- Unnatural-Instructions-GPT4 |
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language: |
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- en |
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library_name: transformers |
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license: other |
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license_link: https://llama.meta.com/llama3/license/ |
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license_name: llama-3 |
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quantized_by: mradermacher |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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--- |
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## About |
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<!-- ### quantize_version: 2 --> |
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<!-- ### output_tensor_quantised: 1 --> |
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<!-- ### convert_type: hf --> |
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<!-- ### vocab_type: --> |
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<!-- ### tags: --> |
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static quants of https://huggingface.co/Replete-AI/Llama3-8B-Instruct-Replete-Adapted |
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<!-- provided-files --> |
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-i1-GGUF |
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## Usage |
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If you are unsure how to use GGUF files, refer to one of [TheBloke's |
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for |
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more details, including on how to concatenate multi-part files. |
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|
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## Provided Quants |
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) |
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| Link | Type | Size/GB | Notes | |
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|:-----|:-----|--------:|:------| |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q2_K.gguf) | Q2_K | 3.3 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.IQ3_XS.gguf) | IQ3_XS | 3.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q3_K_S.gguf) | Q3_K_S | 3.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.IQ3_M.gguf) | IQ3_M | 3.9 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q3_K_L.gguf) | Q3_K_L | 4.4 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.IQ4_XS.gguf) | IQ4_XS | 4.6 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q5_K_S.gguf) | Q5_K_S | 5.7 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q5_K_M.gguf) | Q5_K_M | 5.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q6_K.gguf) | Q6_K | 6.7 | very good quality | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | |
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| [GGUF](https://huggingface.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF/resolve/main/Llama3-8B-Instruct-Replete-Adapted.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | |
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Here is a handy graph by ikawrakow comparing some lower-quality quant |
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types (lower is better): |
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 |
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And here are Artefact2's thoughts on the matter: |
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 |
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## FAQ / Model Request |
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See https://huggingface.co/mradermacher/model_requests for some answers to |
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questions you might have and/or if you want some other model quantized. |
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## Thanks |
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting |
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me use its servers and providing upgrades to my workstation to enable |
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this work in my free time. |
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<!-- end --> |
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