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@@ -1,4 +1,5 @@
1
  ---
 
2
  datasets:
3
  - OpenAssistant/oasst1
4
  - shahules786/orca-best
@@ -7,9 +8,19 @@ language:
7
  - en
8
  license: llama2
9
  model_creator: OpenAssistant
10
- model_link: https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10
11
  model_name: CodeLlama 13B SFT v10
12
  model_type: llama
 
 
 
 
 
 
 
 
 
 
 
13
  quantized_by: TheBloke
14
  ---
15
 
@@ -45,9 +56,9 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
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-oasst-sft-v10-GPTQ)
49
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
50
- * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML)
51
  * [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
52
  <!-- repositories-available end -->
53
 
@@ -65,6 +76,7 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
65
 
66
  <!-- prompt-template end -->
67
 
 
68
  <!-- README_GPTQ.md-provided-files start -->
69
  ## Provided files and GPTQ parameters
70
 
@@ -89,22 +101,22 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
89
 
90
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
91
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
92
- | [main](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
93
- | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
94
- | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
95
- | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
96
- | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-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. |
97
- | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-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. |
98
 
99
  <!-- README_GPTQ.md-provided-files end -->
100
 
101
  <!-- README_GPTQ.md-download-from-branches start -->
102
  ## How to download from branches
103
 
104
- - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:gptq-4bit-32g-actorder_True`
105
  - With Git, you can clone a branch with:
106
  ```
107
- git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ
108
  ```
109
  - In Python Transformers code, the branch is the `revision` parameter; see below.
110
  <!-- README_GPTQ.md-download-from-branches end -->
@@ -117,7 +129,7 @@ It is strongly recommended to use the text-generation-webui one-click-installers
117
 
118
  1. Click the **Model tab**.
119
  2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ`.
120
- - To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:gptq-4bit-32g-actorder_True`
121
  - see Provided Files above for the list of branches for each option.
122
  3. Click **Download**.
123
  4. The model will start downloading. Once it's finished it will say "Done".
@@ -165,10 +177,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
165
 
166
  model_name_or_path = "TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ"
167
  # To use a different branch, change revision
168
- # For example: revision="gptq-4bit-32g-actorder_True"
169
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
170
- torch_dtype=torch.bfloat16,
171
  device_map="auto",
 
172
  revision="main")
173
 
174
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
@@ -185,7 +197,7 @@ prompt_template=f'''<|im_start|>system
185
  print("\n\n*** Generate:")
186
 
187
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
188
- output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
189
  print(tokenizer.decode(output[0]))
190
 
191
  # Inference can also be done using transformers' pipeline
@@ -196,9 +208,11 @@ pipe = pipeline(
196
  model=model,
197
  tokenizer=tokenizer,
198
  max_new_tokens=512,
 
199
  temperature=0.7,
200
  top_p=0.95,
201
- repetition_penalty=1.15
 
202
  )
203
 
204
  print(pipe(prompt_template)[0]['generated_text'])
@@ -223,10 +237,12 @@ For further support, and discussions on these models and AI in general, join us
223
 
224
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
225
 
226
- ## Thanks, and how to contribute.
227
 
228
  Thanks to the [chirper.ai](https://chirper.ai) team!
229
 
 
 
230
  I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
231
 
232
  If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
@@ -238,7 +254,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
238
 
239
  **Special thanks to**: Aemon Algiz.
240
 
241
- **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
242
 
243
 
244
  Thank you to all my generous patrons and donaters!
 
1
  ---
2
+ base_model: https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10
3
  datasets:
4
  - OpenAssistant/oasst1
5
  - shahules786/orca-best
 
8
  - en
9
  license: llama2
10
  model_creator: OpenAssistant
 
11
  model_name: CodeLlama 13B SFT v10
12
  model_type: llama
13
+ prompt_template: '<|im_start|>system
14
+
15
+ {system_message}<|im_end|>
16
+
17
+ <|im_start|>user
18
+
19
+ {prompt}<|im_end|>
20
+
21
+ <|im_start|>assistant
22
+
23
+ '
24
  quantized_by: TheBloke
25
  ---
26
 
 
56
  <!-- repositories-available start -->
57
  ## Repositories available
58
 
59
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-AWQ)
60
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ)
61
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
 
62
  * [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
63
  <!-- repositories-available end -->
64
 
 
76
 
77
  <!-- prompt-template end -->
78
 
79
+
80
  <!-- README_GPTQ.md-provided-files start -->
81
  ## Provided files and GPTQ parameters
82
 
 
101
 
102
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
103
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
104
+ | [main](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. |
105
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
106
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
107
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
108
+ | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-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. |
109
+ | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-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. |
110
 
111
  <!-- README_GPTQ.md-provided-files end -->
112
 
113
  <!-- README_GPTQ.md-download-from-branches start -->
114
  ## How to download from branches
115
 
116
+ - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
117
  - With Git, you can clone a branch with:
118
  ```
119
+ git clone --single-branch --branch main https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ
120
  ```
121
  - In Python Transformers code, the branch is the `revision` parameter; see below.
122
  <!-- README_GPTQ.md-download-from-branches end -->
 
129
 
130
  1. Click the **Model tab**.
131
  2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ`.
132
+ - To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
133
  - see Provided Files above for the list of branches for each option.
134
  3. Click **Download**.
135
  4. The model will start downloading. Once it's finished it will say "Done".
 
177
 
178
  model_name_or_path = "TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ"
179
  # To use a different branch, change revision
180
+ # For example: revision="main"
181
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
182
  device_map="auto",
183
+ trust_remote_code=False,
184
  revision="main")
185
 
186
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
197
  print("\n\n*** Generate:")
198
 
199
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
200
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
201
  print(tokenizer.decode(output[0]))
202
 
203
  # Inference can also be done using transformers' pipeline
 
208
  model=model,
209
  tokenizer=tokenizer,
210
  max_new_tokens=512,
211
+ do_sample=True,
212
  temperature=0.7,
213
  top_p=0.95,
214
+ top_k=40,
215
+ repetition_penalty=1.1
216
  )
217
 
218
  print(pipe(prompt_template)[0]['generated_text'])
 
237
 
238
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
239
 
240
+ ## Thanks, and how to contribute
241
 
242
  Thanks to the [chirper.ai](https://chirper.ai) team!
243
 
244
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
245
+
246
  I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
247
 
248
  If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
 
254
 
255
  **Special thanks to**: Aemon Algiz.
256
 
257
+ **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
258
 
259
 
260
  Thank you to all my generous patrons and donaters!