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@@ -34,41 +34,50 @@ tags:
34
  - Model creator: [Meta](https://huggingface.co/meta-llama)
35
  - Original model: [CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
36
 
 
37
  ## Description
38
 
39
  This repo contains GPTQ model files for [Meta's CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf).
40
 
41
  Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
42
 
 
 
43
  ## Repositories available
44
 
45
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ)
46
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGUF)
47
  * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGML)
48
  * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
 
49
 
 
50
  ## Prompt template: CodeLlama
51
 
52
  ```
53
  [INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
54
  {prompt}
55
  [/INST]
 
56
  ```
57
 
 
 
 
58
  ## Provided files and GPTQ parameters
59
 
60
  Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
61
 
62
  Each separate quant is in a different branch. See below for instructions on fetching from different branches.
63
 
64
- All GPTQ files are made with AutoGPTQ.
65
 
66
  <details>
67
  <summary>Explanation of GPTQ parameters</summary>
68
 
69
  - Bits: The bit size of the quantised model.
70
  - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
71
- - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have issues with models that use Act Order plus Group Size.
72
  - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
73
  - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
74
  - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
@@ -85,6 +94,9 @@ All GPTQ files are made with AutoGPTQ.
85
  | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
86
  | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
87
 
 
 
 
88
  ## How to download from branches
89
 
90
  - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True`
@@ -93,77 +105,77 @@ All GPTQ files are made with AutoGPTQ.
93
  git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ
94
  ```
95
  - In Python Transformers code, the branch is the `revision` parameter; see below.
96
-
 
97
  ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
98
 
99
  Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
100
 
101
- It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
102
 
103
  1. Click the **Model tab**.
104
  2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-Python-GPTQ`.
105
  - To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True`
106
  - see Provided Files above for the list of branches for each option.
107
  3. Click **Download**.
108
- 4. The model will start downloading. Once it's finished it will say "Done"
109
  5. In the top left, click the refresh icon next to **Model**.
110
  6. In the **Model** dropdown, choose the model you just downloaded: `CodeLlama-13B-Python-GPTQ`
111
  7. The model will automatically load, and is now ready for use!
112
  8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
113
- * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
114
  9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
 
115
 
 
116
  ## How to use this GPTQ model from Python code
117
 
118
- First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) 0.3.1 or later installed:
119
 
120
- ```
121
- pip3 install auto-gptq
122
- ```
123
 
124
- If you have problems installing AutoGPTQ, please build from source instead:
 
 
125
  ```
 
 
 
 
126
  pip3 uninstall -y auto-gptq
127
  git clone https://github.com/PanQiWei/AutoGPTQ
128
  cd AutoGPTQ
129
  pip3 install .
130
  ```
131
 
132
- Then try the following example code:
 
 
 
 
 
 
 
 
133
 
134
  ```python
135
- from transformers import AutoTokenizer, pipeline, logging
136
- from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
137
 
138
  model_name_or_path = "TheBloke/CodeLlama-13B-Python-GPTQ"
139
-
140
- use_triton = False
 
 
 
 
141
 
142
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
143
 
144
- model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
145
- use_safetensors=True,
146
- trust_remote_code=True,
147
- device="cuda:0",
148
- use_triton=use_triton,
149
- quantize_config=None)
150
-
151
- """
152
- # To download from a specific branch, use the revision parameter, as in this example:
153
- # Note that `revision` requires AutoGPTQ 0.3.1 or later!
154
-
155
- model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
156
- revision="gptq-4bit-32g-actorder_True",
157
- use_safetensors=True,
158
- trust_remote_code=True,
159
- device="cuda:0",
160
- quantize_config=None)
161
- """
162
-
163
  prompt = "Tell me about AI"
164
  prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
165
  {prompt}
166
  [/INST]
 
167
  '''
168
 
169
  print("\n\n*** Generate:")
@@ -174,9 +186,6 @@ print(tokenizer.decode(output[0]))
174
 
175
  # Inference can also be done using transformers' pipeline
176
 
177
- # Prevent printing spurious transformers error when using pipeline with AutoGPTQ
178
- logging.set_verbosity(logging.CRITICAL)
179
-
180
  print("*** Pipeline:")
181
  pipe = pipeline(
182
  "text-generation",
@@ -190,12 +199,17 @@ pipe = pipeline(
190
 
191
  print(pipe(prompt_template)[0]['generated_text'])
192
  ```
 
193
 
 
194
  ## Compatibility
195
 
196
- The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
 
 
197
 
198
- ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
 
199
 
200
  <!-- footer start -->
201
  <!-- 200823 -->
@@ -220,7 +234,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
220
 
221
  **Special thanks to**: Aemon Algiz.
222
 
223
- **Patreon special mentions**: Kacper Wikieł, knownsqashed, Leonard Tan, Asp the Wyvern, Daniel P. Andersen, Luke Pendergrass, Stanislav Ovsiannikov, RoA, Dave, Ai Maven, Kalila, Will Dee, Imad Khwaja, Nitin Borwankar, Joseph William Delisle, Tony Hughes, Cory Kujawski, Rishabh Srivastava, Russ Johnson, Stephen Murray, Lone Striker, Johann-Peter Hartmann, Elle, J, Deep Realms, SuperWojo, Raven Klaugh, Sebastain Graf, ReadyPlayerEmma, Alps Aficionado, Mano Prime, Derek Yates, Gabriel Puliatti, Mesiah Bishop, Magnesian, Sean Connelly, biorpg, Iucharbius, Olakabola, Fen Risland, Space Cruiser, theTransient, Illia Dulskyi, Thomas Belote, Spencer Kim, Pieter, John Detwiler, Fred von Graf, Michael Davis, Swaroop Kallakuri, subjectnull, Clay Pascal, Subspace Studios, Chris Smitley, Enrico Ros, usrbinkat, Steven Wood, alfie_i, David Ziegler, Willem Michiel, Matthew Berman, Andrey, Pyrater, Jeffrey Morgan, vamX, LangChain4j, Luke @flexchar, Trenton Dambrowitz, Pierre Kircher, Alex, Sam, James Bentley, Edmond Seymore, Eugene Pentland, Pedro Madruga, Rainer Wilmers, Dan Guido, Nathan LeClaire, Spiking Neurons AB, Talal Aujan, zynix, Artur Olbinski, Michael Levine, 阿明, K, John Villwock, Nikolai Manek, Femi Adebogun, senxiiz, Deo Leter, NimbleBox.ai, Viktor Bowallius, Geoffrey Montalvo, Mandus, Ajan Kanaga, ya boyyy, Jonathan Leane, webtim, Brandon Frisco, danny, Alexandros Triantafyllidis, Gabriel Tamborski, Randy H, terasurfer, Vadim, Junyu Yang, Vitor Caleffi, Chadd, transmissions 11
224
 
225
 
226
  Thank you to all my generous patrons and donaters!
@@ -282,7 +296,7 @@ All variants are available in sizes of 7B, 13B and 34B parameters.
282
 
283
  **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/)
284
 
285
- **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)".
286
 
287
  ## Intended Use
288
  **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
 
34
  - Model creator: [Meta](https://huggingface.co/meta-llama)
35
  - Original model: [CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
36
 
37
+ <!-- description start -->
38
  ## Description
39
 
40
  This repo contains GPTQ model files for [Meta's CodeLlama 13B Python](https://huggingface.co/codellama/CodeLlama-13b-python-hf).
41
 
42
  Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
43
 
44
+ <!-- description end -->
45
+ <!-- repositories-available start -->
46
  ## Repositories available
47
 
48
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ)
49
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGUF)
50
  * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGML)
51
  * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
52
+ <!-- repositories-available end -->
53
 
54
+ <!-- prompt-template start -->
55
  ## Prompt template: CodeLlama
56
 
57
  ```
58
  [INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
59
  {prompt}
60
  [/INST]
61
+
62
  ```
63
 
64
+ <!-- prompt-template end -->
65
+
66
+ <!-- README_GPTQ.md-provided-files start -->
67
  ## Provided files and GPTQ parameters
68
 
69
  Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
70
 
71
  Each separate quant is in a different branch. See below for instructions on fetching from different branches.
72
 
73
+ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
74
 
75
  <details>
76
  <summary>Explanation of GPTQ parameters</summary>
77
 
78
  - Bits: The bit size of the quantised model.
79
  - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
80
+ - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
81
  - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
82
  - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
83
  - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
 
94
  | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
95
  | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
96
 
97
+ <!-- README_GPTQ.md-provided-files end -->
98
+
99
+ <!-- README_GPTQ.md-download-from-branches start -->
100
  ## How to download from branches
101
 
102
  - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True`
 
105
  git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/CodeLlama-13B-Python-GPTQ
106
  ```
107
  - In Python Transformers code, the branch is the `revision` parameter; see below.
108
+ <!-- README_GPTQ.md-download-from-branches end -->
109
+ <!-- README_GPTQ.md-text-generation-webui start -->
110
  ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
111
 
112
  Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
113
 
114
+ It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
115
 
116
  1. Click the **Model tab**.
117
  2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-Python-GPTQ`.
118
  - To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-Python-GPTQ:gptq-4bit-32g-actorder_True`
119
  - see Provided Files above for the list of branches for each option.
120
  3. Click **Download**.
121
+ 4. The model will start downloading. Once it's finished it will say "Done".
122
  5. In the top left, click the refresh icon next to **Model**.
123
  6. In the **Model** dropdown, choose the model you just downloaded: `CodeLlama-13B-Python-GPTQ`
124
  7. The model will automatically load, and is now ready for use!
125
  8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
126
+ * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
127
  9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
128
+ <!-- README_GPTQ.md-text-generation-webui end -->
129
 
130
+ <!-- README_GPTQ.md-use-from-python start -->
131
  ## How to use this GPTQ model from Python code
132
 
133
+ ### Install the necessary packages
134
 
135
+ Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
 
 
136
 
137
+ ```shell
138
+ pip3 install transformers>=4.32.0 optimum>=1.12.0
139
+ pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
140
  ```
141
+
142
+ If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
143
+
144
+ ```shell
145
  pip3 uninstall -y auto-gptq
146
  git clone https://github.com/PanQiWei/AutoGPTQ
147
  cd AutoGPTQ
148
  pip3 install .
149
  ```
150
 
151
+ ### For CodeLlama models only: you must use Transformers 4.33.0 or later.
152
+
153
+ If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
154
+ ```shell
155
+ pip3 uninstall -y transformers
156
+ pip3 install git+https://github.com/huggingface/transformers.git
157
+ ```
158
+
159
+ ### You can then use the following code
160
 
161
  ```python
162
+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
163
 
164
  model_name_or_path = "TheBloke/CodeLlama-13B-Python-GPTQ"
165
+ # To use a different branch, change revision
166
+ # For example: revision="gptq-4bit-32g-actorder_True"
167
+ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
168
+ torch_dtype=torch.float16,
169
+ device_map="auto",
170
+ revision="main")
171
 
172
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
173
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
174
  prompt = "Tell me about AI"
175
  prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
176
  {prompt}
177
  [/INST]
178
+
179
  '''
180
 
181
  print("\n\n*** Generate:")
 
186
 
187
  # Inference can also be done using transformers' pipeline
188
 
 
 
 
189
  print("*** Pipeline:")
190
  pipe = pipeline(
191
  "text-generation",
 
199
 
200
  print(pipe(prompt_template)[0]['generated_text'])
201
  ```
202
+ <!-- README_GPTQ.md-use-from-python end -->
203
 
204
+ <!-- README_GPTQ.md-compatibility start -->
205
  ## Compatibility
206
 
207
+ The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
208
+
209
+ [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
210
 
211
+ [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
212
+ <!-- README_GPTQ.md-compatibility end -->
213
 
214
  <!-- footer start -->
215
  <!-- 200823 -->
 
234
 
235
  **Special thanks to**: Aemon Algiz.
236
 
237
+ **Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser
238
 
239
 
240
  Thank you to all my generous patrons and donaters!
 
296
 
297
  **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/)
298
 
299
+ **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
300
 
301
  ## Intended Use
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  **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.