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1
  ---
 
2
  datasets:
3
  - totally-not-an-llm/EverythingLM-data
4
  inference: false
5
  license: llama2
6
  model_creator: Kai Howard
7
- model_link: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
8
  model_name: EverythingLM 13B 16K
9
  model_type: llama
 
 
 
 
 
 
 
 
10
  quantized_by: TheBloke
11
  ---
12
 
@@ -42,9 +50,9 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
42
  <!-- repositories-available start -->
43
  ## Repositories available
44
 
 
45
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ)
46
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GGUF)
47
- * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GGML)
48
  * [Kai Howard's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k)
49
  <!-- repositories-available end -->
50
 
@@ -61,6 +69,7 @@ ASSISTANT:
61
 
62
  <!-- prompt-template end -->
63
 
 
64
  <!-- README_GPTQ.md-provided-files start -->
65
  ## Provided files and GPTQ parameters
66
 
@@ -85,22 +94,22 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
85
 
86
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
87
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
88
- | [main](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.26 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
89
- | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 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. |
90
- | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 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. |
91
- | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 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. |
92
- | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
93
- | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 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. |
94
 
95
  <!-- README_GPTQ.md-provided-files end -->
96
 
97
  <!-- README_GPTQ.md-download-from-branches start -->
98
  ## How to download from branches
99
 
100
- - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/EverythingLM-13B-16K-GPTQ:gptq-4bit-32g-actorder_True`
101
  - With Git, you can clone a branch with:
102
  ```
103
- git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ
104
  ```
105
  - In Python Transformers code, the branch is the `revision` parameter; see below.
106
  <!-- README_GPTQ.md-download-from-branches end -->
@@ -113,7 +122,7 @@ It is strongly recommended to use the text-generation-webui one-click-installers
113
 
114
  1. Click the **Model tab**.
115
  2. Under **Download custom model or LoRA**, enter `TheBloke/EverythingLM-13B-16K-GPTQ`.
116
- - To download from a specific branch, enter for example `TheBloke/EverythingLM-13B-16K-GPTQ:gptq-4bit-32g-actorder_True`
117
  - see Provided Files above for the list of branches for each option.
118
  3. Click **Download**.
119
  4. The model will start downloading. Once it's finished it will say "Done".
@@ -161,10 +170,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
161
 
162
  model_name_or_path = "TheBloke/EverythingLM-13B-16K-GPTQ"
163
  # To use a different branch, change revision
164
- # For example: revision="gptq-4bit-32g-actorder_True"
165
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
166
- torch_dtype=torch.float16,
167
  device_map="auto",
 
168
  revision="main")
169
 
170
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
@@ -180,7 +189,7 @@ ASSISTANT:
180
  print("\n\n*** Generate:")
181
 
182
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
183
- output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
184
  print(tokenizer.decode(output[0]))
185
 
186
  # Inference can also be done using transformers' pipeline
@@ -191,9 +200,11 @@ pipe = pipeline(
191
  model=model,
192
  tokenizer=tokenizer,
193
  max_new_tokens=512,
 
194
  temperature=0.7,
195
  top_p=0.95,
196
- repetition_penalty=1.15
 
197
  )
198
 
199
  print(pipe(prompt_template)[0]['generated_text'])
@@ -218,10 +229,12 @@ For further support, and discussions on these models and AI in general, join us
218
 
219
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
220
 
221
- ## Thanks, and how to contribute.
222
 
223
  Thanks to the [chirper.ai](https://chirper.ai) team!
224
 
 
 
225
  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.
226
 
227
  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.
@@ -233,7 +246,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
233
 
234
  **Special thanks to**: Aemon Algiz.
235
 
236
- **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
237
 
238
 
239
  Thank you to all my generous patrons and donaters!
 
1
  ---
2
+ base_model: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
3
  datasets:
4
  - totally-not-an-llm/EverythingLM-data
5
  inference: false
6
  license: llama2
7
  model_creator: Kai Howard
 
8
  model_name: EverythingLM 13B 16K
9
  model_type: llama
10
+ prompt_template: 'You are a helpful AI assistant.
11
+
12
+
13
+ USER: {prompt}
14
+
15
+ ASSISTANT:
16
+
17
+ '
18
  quantized_by: TheBloke
19
  ---
20
 
 
50
  <!-- repositories-available start -->
51
  ## Repositories available
52
 
53
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/EverythingLM-13B-16K-AWQ)
54
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ)
55
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GGUF)
 
56
  * [Kai Howard's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k)
57
  <!-- repositories-available end -->
58
 
 
69
 
70
  <!-- prompt-template end -->
71
 
72
+
73
  <!-- README_GPTQ.md-provided-files start -->
74
  ## Provided files and GPTQ parameters
75
 
 
94
 
95
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
96
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
97
+ | [main](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. |
98
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
99
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
100
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 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. |
101
+ | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
102
+ | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
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/EverythingLM-13B-16K-GPTQ:main`
110
  - With Git, you can clone a branch with:
111
  ```
112
+ git clone --single-branch --branch main https://huggingface.co/TheBloke/EverythingLM-13B-16K-GPTQ
113
  ```
114
  - In Python Transformers code, the branch is the `revision` parameter; see below.
115
  <!-- README_GPTQ.md-download-from-branches end -->
 
122
 
123
  1. Click the **Model tab**.
124
  2. Under **Download custom model or LoRA**, enter `TheBloke/EverythingLM-13B-16K-GPTQ`.
125
+ - To download from a specific branch, enter for example `TheBloke/EverythingLM-13B-16K-GPTQ:main`
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
 
171
  model_name_or_path = "TheBloke/EverythingLM-13B-16K-GPTQ"
172
  # To use a different branch, change revision
173
+ # For example: revision="main"
174
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
175
  device_map="auto",
176
+ trust_remote_code=False,
177
  revision="main")
178
 
179
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
189
  print("\n\n*** Generate:")
190
 
191
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
192
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
193
  print(tokenizer.decode(output[0]))
194
 
195
  # Inference can also be done using transformers' pipeline
 
200
  model=model,
201
  tokenizer=tokenizer,
202
  max_new_tokens=512,
203
+ do_sample=True,
204
  temperature=0.7,
205
  top_p=0.95,
206
+ top_k=40,
207
+ repetition_penalty=1.1
208
  )
209
 
210
  print(pipe(prompt_template)[0]['generated_text'])
 
229
 
230
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
231
 
232
+ ## Thanks, and how to contribute
233
 
234
  Thanks to the [chirper.ai](https://chirper.ai) team!
235
 
236
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
237
+
238
  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.
239
 
240
  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.
 
246
 
247
  **Special thanks to**: Aemon Algiz.
248
 
249
+ **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
250
 
251
 
252
  Thank you to all my generous patrons and donaters!