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@@ -1,12 +1,25 @@
1
  ---
 
2
  inference: false
3
  language:
4
  - en
5
- license: llama2
 
6
  model_creator: NousResearch
7
- model_link: https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b
8
  model_name: Nous Hermes Llama 2 7B
9
  model_type: llama
 
 
 
 
 
 
 
 
 
 
 
 
10
  quantized_by: TheBloke
11
  tags:
12
  - llama-2
@@ -47,9 +60,9 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
47
  <!-- repositories-available start -->
48
  ## Repositories available
49
 
 
50
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ)
51
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GGUF)
52
- * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GGML)
53
  * [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)
54
  <!-- repositories-available end -->
55
 
@@ -67,7 +80,15 @@ Below is an instruction that describes a task. Write a response that appropriate
67
  ```
68
 
69
  <!-- prompt-template end -->
 
 
 
 
70
 
 
 
 
 
71
  <!-- README_GPTQ.md-provided-files start -->
72
  ## Provided files and GPTQ parameters
73
 
@@ -92,13 +113,13 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
92
 
93
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
94
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
95
- | [main](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
96
- | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
97
- | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.02 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. |
98
- | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 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. |
99
- | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
100
  | [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
101
- | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
102
  | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.31 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. |
103
 
104
  <!-- README_GPTQ.md-provided-files end -->
@@ -106,10 +127,10 @@ All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches
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/Nous-Hermes-Llama-2-7B-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/Nous-Hermes-Llama-2-7B-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 +143,7 @@ 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/Nous-Hermes-Llama-2-7B-GPTQ`.
125
- - To download from a specific branch, enter for example `TheBloke/Nous-Hermes-Llama-2-7B-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 +191,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
170
 
171
  model_name_or_path = "TheBloke/Nous-Hermes-Llama-2-7B-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.bfloat16,
176
  device_map="auto",
 
177
  revision="main")
178
 
179
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
@@ -191,7 +212,7 @@ prompt_template=f'''Below is an instruction that describes a task. Write a respo
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, max_new_tokens=512)
195
  print(tokenizer.decode(output[0]))
196
 
197
  # Inference can also be done using transformers' pipeline
@@ -202,9 +223,11 @@ pipe = pipeline(
202
  model=model,
203
  tokenizer=tokenizer,
204
  max_new_tokens=512,
 
205
  temperature=0.7,
206
  top_p=0.95,
207
- repetition_penalty=1.15
 
208
  )
209
 
210
  print(pipe(prompt_template)[0]['generated_text'])
@@ -229,10 +252,12 @@ For further support, and discussions on these models and AI in general, join us
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
  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.
237
 
238
  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.
@@ -244,7 +269,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
244
 
245
  **Special thanks to**: Aemon Algiz.
246
 
247
- **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
248
 
249
 
250
  Thank you to all my generous patrons and donaters!
@@ -276,7 +301,7 @@ The model was trained almost entirely on synthetic GPT-4 outputs. Curating high
276
  This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
277
 
278
  ## Collaborators
279
- The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI.
280
 
281
  Special mention goes to @winglian for assisting in some of the training issues.
282
 
@@ -316,11 +341,34 @@ or
316
 
317
  ### Response:
318
  <leave a newline blank for model to respond>
 
319
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
320
  ```
321
 
322
  ## Benchmark Results
323
- Coming soon
 
 
324
 
325
  ## Resources for Applied Use Cases:
326
  For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord
 
1
  ---
2
+ base_model: https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b
3
  inference: false
4
  language:
5
  - en
6
+ license:
7
+ - mit
8
  model_creator: NousResearch
 
9
  model_name: Nous Hermes Llama 2 7B
10
  model_type: llama
11
+ prompt_template: 'Below is an instruction that describes a task. Write a response
12
+ that appropriately completes the request.
13
+
14
+
15
+ ### Instruction:
16
+
17
+ {prompt}
18
+
19
+
20
+ ### Response:
21
+
22
+ '
23
  quantized_by: TheBloke
24
  tags:
25
  - llama-2
 
60
  <!-- repositories-available start -->
61
  ## Repositories available
62
 
63
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-AWQ)
64
  * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ)
65
  * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GGUF)
 
66
  * [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)
67
  <!-- repositories-available end -->
68
 
 
80
  ```
81
 
82
  <!-- prompt-template end -->
83
+ <!-- licensing start -->
84
+ ## Licensing
85
+
86
+ The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license.
87
 
88
+ As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
89
+
90
+ In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [NousResearch's Nous Hermes Llama 2 7B](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b).
91
+ <!-- licensing end -->
92
  <!-- README_GPTQ.md-provided-files start -->
93
  ## Provided files and GPTQ parameters
94
 
 
113
 
114
  | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
115
  | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
116
+ | [main](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, without Act Order and group size 128g. |
117
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
118
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
119
+ | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
120
+ | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
121
  | [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
122
+ | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
123
  | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.31 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. |
124
 
125
  <!-- README_GPTQ.md-provided-files end -->
 
127
  <!-- README_GPTQ.md-download-from-branches start -->
128
  ## How to download from branches
129
 
130
+ - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Nous-Hermes-Llama-2-7B-GPTQ:main`
131
  - With Git, you can clone a branch with:
132
  ```
133
+ git clone --single-branch --branch main https://huggingface.co/TheBloke/Nous-Hermes-Llama-2-7B-GPTQ
134
  ```
135
  - In Python Transformers code, the branch is the `revision` parameter; see below.
136
  <!-- README_GPTQ.md-download-from-branches end -->
 
143
 
144
  1. Click the **Model tab**.
145
  2. Under **Download custom model or LoRA**, enter `TheBloke/Nous-Hermes-Llama-2-7B-GPTQ`.
146
+ - To download from a specific branch, enter for example `TheBloke/Nous-Hermes-Llama-2-7B-GPTQ:main`
147
  - see Provided Files above for the list of branches for each option.
148
  3. Click **Download**.
149
  4. The model will start downloading. Once it's finished it will say "Done".
 
191
 
192
  model_name_or_path = "TheBloke/Nous-Hermes-Llama-2-7B-GPTQ"
193
  # To use a different branch, change revision
194
+ # For example: revision="main"
195
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
196
  device_map="auto",
197
+ trust_remote_code=False,
198
  revision="main")
199
 
200
  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
212
  print("\n\n*** Generate:")
213
 
214
  input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
215
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
216
  print(tokenizer.decode(output[0]))
217
 
218
  # Inference can also be done using transformers' pipeline
 
223
  model=model,
224
  tokenizer=tokenizer,
225
  max_new_tokens=512,
226
+ do_sample=True,
227
  temperature=0.7,
228
  top_p=0.95,
229
+ top_k=40,
230
+ repetition_penalty=1.1
231
  )
232
 
233
  print(pipe(prompt_template)[0]['generated_text'])
 
252
 
253
  [TheBloke AI's Discord server](https://discord.gg/theblokeai)
254
 
255
+ ## Thanks, and how to contribute
256
 
257
  Thanks to the [chirper.ai](https://chirper.ai) team!
258
 
259
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
260
+
261
  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.
262
 
263
  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.
 
269
 
270
  **Special thanks to**: Aemon Algiz.
271
 
272
+ **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
273
 
274
 
275
  Thank you to all my generous patrons and donaters!
 
301
  This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
302
 
303
  ## Collaborators
304
+ The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art and Redmond AI.
305
 
306
  Special mention goes to @winglian for assisting in some of the training issues.
307
 
 
341
 
342
  ### Response:
343
  <leave a newline blank for model to respond>
344
+ ```
345
 
346
+ AGIEval
347
+ ```
348
+ | Task |Version| Metric |Value | |Stderr|
349
+ |------------------------------|------:|--------|-----:|---|-----:|
350
+ |agieval_aqua_rat | 0|acc |0.2520|± |0.0273|
351
+ | | |acc_norm|0.2402|± |0.0269|
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+ |agieval_logiqa_en | 0|acc |0.2796|± |0.0176|
353
+ | | |acc_norm|0.3241|± |0.0184|
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+ |agieval_lsat_ar | 0|acc |0.2478|± |0.0285|
355
+ | | |acc_norm|0.2348|± |0.0280|
356
+ |agieval_lsat_lr | 0|acc |0.2843|± |0.0200|
357
+ | | |acc_norm|0.2765|± |0.0198|
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+ |agieval_lsat_rc | 0|acc |0.3271|± |0.0287|
359
+ | | |acc_norm|0.3011|± |0.0280|
360
+ |agieval_sat_en | 0|acc |0.4660|± |0.0348|
361
+ | | |acc_norm|0.4223|± |0.0345|
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+ |agieval_sat_en_without_passage| 0|acc |0.3738|± |0.0338|
363
+ | | |acc_norm|0.3447|± |0.0332|
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+ |agieval_sat_math | 0|acc |0.2500|± |0.0293|
365
+ | | |acc_norm|0.2364|± |0.0287|
366
  ```
367
 
368
  ## Benchmark Results
369
+
370
+
371
+
372
 
373
  ## Resources for Applied Use Cases:
374
  For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord