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@@ -1,4 +1,5 @@
1
  ---
 
2
  inference: false
3
  license: llama2
4
  model-index:
@@ -15,9 +16,11 @@ model-index:
15
  task:
16
  type: text-generation
17
  model_creator: Phind
18
- model_link: https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1
19
  model_name: Phind CodeLlama 34B Python v1
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  model_type: llama
 
 
 
21
  quantized_by: TheBloke
22
  tags:
23
  - code llama
@@ -55,6 +58,7 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
55
  <!-- repositories-available start -->
56
  ## Repositories available
57
 
 
58
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ)
59
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF)
60
  * [Phind's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1)
@@ -70,6 +74,7 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
70
 
71
  <!-- prompt-template end -->
72
 
 
73
  <!-- README_GPTQ.md-provided-files start -->
74
  ## Provided files and GPTQ parameters
75
 
@@ -94,22 +99,22 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
94
 
95
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
96
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
97
- | [main](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 17.69 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
98
- | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 20.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
99
- | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 18.98 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. |
100
- | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 18.33 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. |
101
  | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.54 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
102
- | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 14.14 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False but poor AutoGPTQ CUDA speed. |
103
 
104
  <!-- README_GPTQ.md-provided-files end -->
105
 
106
  <!-- README_GPTQ.md-download-from-branches start -->
107
  ## How to download from branches
108
 
109
- - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ:gptq-4bit-32g-actorder_True`
110
  - With Git, you can clone a branch with:
111
  ```
112
- git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ
113
  ```
114
  - In Python Transformers code, the branch is the `revision` parameter; see below.
115
  <!-- README_GPTQ.md-download-from-branches end -->
@@ -122,7 +127,7 @@ It is strongly recommended to use the text-generation-webui one-click-installers
122
 
123
  1. Click the **Model tab**.
124
  2. Under **Download custom model or LoRA**, enter `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ`.
125
- - To download from a specific branch, enter for example `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ:gptq-4bit-32g-actorder_True`
126
  - see Provided Files above for the list of branches for each option.
127
  3. Click **Download**.
128
  4. The model will start downloading. Once it's finished it will say "Done".
@@ -170,10 +175,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
170
 
171
  model_name_or_path = "TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ"
172
  # To use a different branch, change revision
173
- # For example: revision="gptq-4bit-32g-actorder_True"
174
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
175
- torch_dtype=torch.float16,
176
  device_map="auto",
 
177
  revision="main")
178
 
179
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
@@ -186,7 +191,7 @@ prompt_template=f'''{prompt} \n
186
  print("\n\n*** Generate:")
187
 
188
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
189
- output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
190
  print(tokenizer.decode(output[0]))
191
 
192
  # Inference can also be done using transformers' pipeline
@@ -197,9 +202,11 @@ pipe = pipeline(
197
  model=model,
198
  tokenizer=tokenizer,
199
  max_new_tokens=512,
 
200
  temperature=0.7,
201
  top_p=0.95,
202
- repetition_penalty=1.15
 
203
  )
204
 
205
  print(pipe(prompt_template)[0]['generated_text'])
@@ -224,10 +231,12 @@ For further support, and discussions on these models and AI in general, join us
224
 
225
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
226
 
227
- ## Thanks, and how to contribute.
228
 
229
  Thanks to the [chirper.ai](https://chirper.ai) team!
230
 
 
 
231
  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.
232
 
233
  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.
@@ -239,7 +248,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
239
 
240
  **Special thanks to**: Aemon Algiz.
241
 
242
- **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
243
 
244
 
245
  Thank you to all my generous patrons and donaters!
 
1
  ---
2
+ base_model: https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1
3
  inference: false
4
  license: llama2
5
  model-index:
 
16
  task:
17
  type: text-generation
18
  model_creator: Phind
 
19
  model_name: Phind CodeLlama 34B Python v1
20
  model_type: llama
21
+ prompt_template: '{prompt} \n
22
+
23
+ '
24
  quantized_by: TheBloke
25
  tags:
26
  - code llama
 
58
  <!-- repositories-available start -->
59
  ## Repositories available
60
 
61
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ)
62
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ)
63
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF)
64
  * [Phind's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1)
 
74
 
75
  <!-- prompt-template end -->
76
 
77
+
78
  <!-- README_GPTQ.md-provided-files start -->
79
  ## Provided files and GPTQ parameters
80
 
 
99
 
100
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
101
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
102
+ | [main](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 17.69 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
103
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 20.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
104
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 18.98 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
105
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-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 | 18.33 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
106
  | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 13.54 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
107
+ | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 14.14 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
108
 
109
  <!-- README_GPTQ.md-provided-files end -->
110
 
111
  <!-- README_GPTQ.md-download-from-branches start -->
112
  ## How to download from branches
113
 
114
+ - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ:main`
115
  - With Git, you can clone a branch with:
116
  ```
117
+ git clone --single-branch --branch main https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ
118
  ```
119
  - In Python Transformers code, the branch is the `revision` parameter; see below.
120
  <!-- README_GPTQ.md-download-from-branches end -->
 
127
 
128
  1. Click the **Model tab**.
129
  2. Under **Download custom model or LoRA**, enter `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ`.
130
+ - To download from a specific branch, enter for example `TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ:main`
131
  - see Provided Files above for the list of branches for each option.
132
  3. Click **Download**.
133
  4. The model will start downloading. Once it's finished it will say "Done".
 
175
 
176
  model_name_or_path = "TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ"
177
  # To use a different branch, change revision
178
+ # For example: revision="main"
179
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
180
  device_map="auto",
181
+ trust_remote_code=False,
182
  revision="main")
183
 
184
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
191
  print("\n\n*** Generate:")
192
 
193
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
194
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
195
  print(tokenizer.decode(output[0]))
196
 
197
  # Inference can also be done using transformers' pipeline
 
202
  model=model,
203
  tokenizer=tokenizer,
204
  max_new_tokens=512,
205
+ do_sample=True,
206
  temperature=0.7,
207
  top_p=0.95,
208
+ top_k=40,
209
+ repetition_penalty=1.1
210
  )
211
 
212
  print(pipe(prompt_template)[0]['generated_text'])
 
231
 
232
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
233
 
234
+ ## Thanks, and how to contribute
235
 
236
  Thanks to the [chirper.ai](https://chirper.ai) team!
237
 
238
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
239
+
240
  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.
241
 
242
  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.
 
248
 
249
  **Special thanks to**: Aemon Algiz.
250
 
251
+ **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
252
 
253
 
254
  Thank you to all my generous patrons and donaters!