Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
4-bit precision
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@@ -1,4 +1,5 @@
1
  ---
 
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  datasets:
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  - conceptofmind/cot_submix_original
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  - conceptofmind/flan2021_submix_original
@@ -9,10 +10,22 @@ language:
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  - en
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  license: llama2
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  model_creator: Stability AI
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- model_link: https://huggingface.co/stabilityai/StableBeluga2
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  model_name: StableBeluga2
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  model_type: llama
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  pipeline_tag: text-generation
 
 
 
 
 
 
 
 
 
 
 
 
 
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  quantized_by: TheBloke
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  ---
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@@ -48,9 +61,9 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
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  <!-- repositories-available start -->
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  ## Repositories available
50
 
 
51
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ)
52
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/StableBeluga2-70B-GGUF)
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- * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/StableBeluga2-70B-GGML)
54
  * [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/StableBeluga2)
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  <!-- repositories-available end -->
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@@ -70,6 +83,7 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
70
 
71
  <!-- prompt-template end -->
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73
  <!-- README_GPTQ.md-provided-files start -->
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  ## Provided files and GPTQ parameters
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@@ -94,15 +108,15 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
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95
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
96
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
97
- | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
98
- | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 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. |
99
- | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 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. |
100
- | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False but poor AutoGPTQ CUDA speed. |
101
  | [gptq-3bit-128g-actorder_False](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-128g-actorder_False) | 3 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
102
  | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.78 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
103
  | [gptq-4bit-128g-actorder_False](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-128g-actorder_False) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, without Act Order and group size 128g. |
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- | [gptq-3bit-64g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-64g-actorder_True) | 3 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 29.30 GB | No | 3-bit, with group size 64g and act-order. Poor AutoGPTQ CUDA speed. |
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- | [main](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/main) | 4 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
106
 
107
  <!-- README_GPTQ.md-provided-files end -->
108
 
@@ -175,8 +189,8 @@ model_name_or_path = "TheBloke/StableBeluga2-70B-GPTQ"
175
  # To use a different branch, change revision
176
  # For example: revision="gptq-4bit-32g-actorder_True"
177
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
178
- torch_dtype=torch.float16,
179
  device_map="auto",
 
180
  revision="main")
181
 
182
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
@@ -195,7 +209,7 @@ prompt_template=f'''### System:
195
  print("\n\n*** Generate:")
196
 
197
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
198
- output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
199
  print(tokenizer.decode(output[0]))
200
 
201
  # Inference can also be done using transformers' pipeline
@@ -206,9 +220,11 @@ pipe = pipeline(
206
  model=model,
207
  tokenizer=tokenizer,
208
  max_new_tokens=512,
 
209
  temperature=0.7,
210
  top_p=0.95,
211
- repetition_penalty=1.15
 
212
  )
213
 
214
  print(pipe(prompt_template)[0]['generated_text'])
@@ -233,10 +249,12 @@ For further support, and discussions on these models and AI in general, join us
233
 
234
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
235
 
236
- ## Thanks, and how to contribute.
237
 
238
  Thanks to the [chirper.ai](https://chirper.ai) team!
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.
@@ -248,7 +266,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
248
 
249
  **Special thanks to**: Aemon Algiz.
250
 
251
- **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
252
 
253
 
254
  Thank you to all my generous patrons and donaters!
 
1
  ---
2
+ base_model: https://huggingface.co/stabilityai/StableBeluga2
3
  datasets:
4
  - conceptofmind/cot_submix_original
5
  - conceptofmind/flan2021_submix_original
 
10
  - en
11
  license: llama2
12
  model_creator: Stability AI
 
13
  model_name: StableBeluga2
14
  model_type: llama
15
  pipeline_tag: text-generation
16
+ prompt_template: '### System:
17
+
18
+ {system_message}
19
+
20
+
21
+ ### User:
22
+
23
+ {prompt}
24
+
25
+
26
+ ### Assistant:
27
+
28
+ '
29
  quantized_by: TheBloke
30
  ---
31
 
 
61
  <!-- repositories-available start -->
62
  ## Repositories available
63
 
64
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/StableBeluga2-70B-AWQ)
65
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ)
66
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/StableBeluga2-70B-GGUF)
 
67
  * [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/StableBeluga2)
68
  <!-- repositories-available end -->
69
 
 
83
 
84
  <!-- prompt-template end -->
85
 
86
+
87
  <!-- README_GPTQ.md-provided-files start -->
88
  ## Provided files and GPTQ parameters
89
 
 
108
 
109
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
110
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
111
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
112
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
113
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
114
+ | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
115
  | [gptq-3bit-128g-actorder_False](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-128g-actorder_False) | 3 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
116
  | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.78 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
117
  | [gptq-4bit-128g-actorder_False](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-4bit-128g-actorder_False) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, without Act Order and group size 128g. |
118
+ | [gptq-3bit-64g-actorder_True](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/gptq-3bit-64g-actorder_True) | 3 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 29.30 GB | No | 3-bit, with group size 64g and act-order. |
119
+ | [main](https://huggingface.co/TheBloke/StableBeluga2-70B-GPTQ/tree/main) | 4 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
120
 
121
  <!-- README_GPTQ.md-provided-files end -->
122
 
 
189
  # To use a different branch, change revision
190
  # For example: revision="gptq-4bit-32g-actorder_True"
191
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
192
  device_map="auto",
193
+ trust_remote_code=False,
194
  revision="main")
195
 
196
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
209
  print("\n\n*** Generate:")
210
 
211
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
212
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
213
  print(tokenizer.decode(output[0]))
214
 
215
  # Inference can also be done using transformers' pipeline
 
220
  model=model,
221
  tokenizer=tokenizer,
222
  max_new_tokens=512,
223
+ do_sample=True,
224
  temperature=0.7,
225
  top_p=0.95,
226
+ top_k=40,
227
+ repetition_penalty=1.1
228
  )
229
 
230
  print(pipe(prompt_template)[0]['generated_text'])
 
249
 
250
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
251
 
252
+ ## Thanks, and how to contribute
253
 
254
  Thanks to the [chirper.ai](https://chirper.ai) team!
255
 
256
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
257
+
258
  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.
259
 
260
  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.
 
266
 
267
  **Special thanks to**: Aemon Algiz.
268
 
269
+ **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
270
 
271
 
272
  Thank you to all my generous patrons and donaters!