TheBloke commited on
Commit
fd8ad68
1 Parent(s): 5e6c4e5

Upload README.md

Browse files
Files changed (1) hide show
  1. README.md +487 -0
README.md ADDED
@@ -0,0 +1,487 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
3
+ datasets:
4
+ - mlabonne/chatml_dpo_pairs
5
+ inference: false
6
+ language:
7
+ - en
8
+ license: apache-2.0
9
+ model_creator: Maxime Labonne
10
+ model_name: NeuralHermes 2.5 Mistral 7B
11
+ model_type: mistral
12
+ prompt_template: '<|im_start|>system
13
+
14
+ {system_message}<|im_end|>
15
+
16
+ <|im_start|>user
17
+
18
+ {prompt}<|im_end|>
19
+
20
+ <|im_start|>assistant
21
+
22
+ '
23
+ quantized_by: TheBloke
24
+ tags:
25
+ - mistral
26
+ - instruct
27
+ - finetune
28
+ - chatml
29
+ - gpt4
30
+ - synthetic data
31
+ - distillation
32
+ - dpo
33
+ - rlhf
34
+ ---
35
+ <!-- markdownlint-disable MD041 -->
36
+
37
+ <!-- header start -->
38
+ <!-- 200823 -->
39
+ <div style="width: auto; margin-left: auto; margin-right: auto">
40
+ <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
41
+ </div>
42
+ <div style="display: flex; justify-content: space-between; width: 100%;">
43
+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
44
+ <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
45
+ </div>
46
+ <div style="display: flex; flex-direction: column; align-items: flex-end;">
47
+ <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
48
+ </div>
49
+ </div>
50
+ <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
51
+ <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
52
+ <!-- header end -->
53
+
54
+ # NeuralHermes 2.5 Mistral 7B - GPTQ
55
+ - Model creator: [Maxime Labonne](https://huggingface.co/mlabonne)
56
+ - Original model: [NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
57
+
58
+ <!-- description start -->
59
+ # Description
60
+
61
+ This repo contains GPTQ model files for [Maxime Labonne's NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B).
62
+
63
+ Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
64
+
65
+ These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
66
+
67
+ <!-- description end -->
68
+ <!-- repositories-available start -->
69
+ ## Repositories available
70
+
71
+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ)
72
+ * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ)
73
+ * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF)
74
+ * [Maxime Labonne's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
75
+ <!-- repositories-available end -->
76
+
77
+ <!-- prompt-template start -->
78
+ ## Prompt template: ChatML
79
+
80
+ ```
81
+ <|im_start|>system
82
+ {system_message}<|im_end|>
83
+ <|im_start|>user
84
+ {prompt}<|im_end|>
85
+ <|im_start|>assistant
86
+
87
+ ```
88
+
89
+ <!-- prompt-template end -->
90
+
91
+
92
+
93
+ <!-- README_GPTQ.md-compatible clients start -->
94
+ ## Known compatible clients / servers
95
+
96
+ These GPTQ models are known to work in the following inference servers/webuis.
97
+
98
+ - [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
99
+ - [KoboldAI United](https://github.com/henk717/koboldai)
100
+ - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
101
+ - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
102
+
103
+ This may not be a complete list; if you know of others, please let me know!
104
+ <!-- README_GPTQ.md-compatible clients end -->
105
+
106
+ <!-- README_GPTQ.md-provided-files start -->
107
+ ## Provided files, and GPTQ parameters
108
+
109
+ Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
110
+
111
+ Each separate quant is in a different branch. See below for instructions on fetching from different branches.
112
+
113
+ Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
114
+
115
+ <details>
116
+ <summary>Explanation of GPTQ parameters</summary>
117
+
118
+ - Bits: The bit size of the quantised model.
119
+ - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
120
+ - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
121
+ - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
122
+ - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
123
+ - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
124
+ - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
125
+
126
+ </details>
127
+
128
+ | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
129
+ | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
130
+ | [main](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
131
+ | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
132
+ | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
133
+ | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
134
+ | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
135
+ | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
136
+
137
+ <!-- README_GPTQ.md-provided-files end -->
138
+
139
+ <!-- README_GPTQ.md-download-from-branches start -->
140
+ ## How to download, including from branches
141
+
142
+ ### In text-generation-webui
143
+
144
+ To download from the `main` branch, enter `TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ` in the "Download model" box.
145
+
146
+ To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ:gptq-4bit-32g-actorder_True`
147
+
148
+ ### From the command line
149
+
150
+ I recommend using the `huggingface-hub` Python library:
151
+
152
+ ```shell
153
+ pip3 install huggingface-hub
154
+ ```
155
+
156
+ To download the `main` branch to a folder called `NeuralHermes-2.5-Mistral-7B-GPTQ`:
157
+
158
+ ```shell
159
+ mkdir NeuralHermes-2.5-Mistral-7B-GPTQ
160
+ huggingface-cli download TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ --local-dir NeuralHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False
161
+ ```
162
+
163
+ To download from a different branch, add the `--revision` parameter:
164
+
165
+ ```shell
166
+ mkdir NeuralHermes-2.5-Mistral-7B-GPTQ
167
+ huggingface-cli download TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir NeuralHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False
168
+ ```
169
+
170
+ <details>
171
+ <summary>More advanced huggingface-cli download usage</summary>
172
+
173
+ If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
174
+
175
+ The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
176
+
177
+ For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
178
+
179
+ To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
180
+
181
+ ```shell
182
+ pip3 install hf_transfer
183
+ ```
184
+
185
+ And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
186
+
187
+ ```shell
188
+ mkdir NeuralHermes-2.5-Mistral-7B-GPTQ
189
+ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ --local-dir NeuralHermes-2.5-Mistral-7B-GPTQ --local-dir-use-symlinks False
190
+ ```
191
+
192
+ Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
193
+ </details>
194
+
195
+ ### With `git` (**not** recommended)
196
+
197
+ To clone a specific branch with `git`, use a command like this:
198
+
199
+ ```shell
200
+ git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ
201
+ ```
202
+
203
+ Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
204
+
205
+ <!-- README_GPTQ.md-download-from-branches end -->
206
+ <!-- README_GPTQ.md-text-generation-webui start -->
207
+ ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
208
+
209
+ Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
210
+
211
+ It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
212
+
213
+ 1. Click the **Model tab**.
214
+ 2. Under **Download custom model or LoRA**, enter `TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ`.
215
+
216
+ - To download from a specific branch, enter for example `TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ:gptq-4bit-32g-actorder_True`
217
+ - see Provided Files above for the list of branches for each option.
218
+
219
+ 3. Click **Download**.
220
+ 4. The model will start downloading. Once it's finished it will say "Done".
221
+ 5. In the top left, click the refresh icon next to **Model**.
222
+ 6. In the **Model** dropdown, choose the model you just downloaded: `NeuralHermes-2.5-Mistral-7B-GPTQ`
223
+ 7. The model will automatically load, and is now ready for use!
224
+ 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
225
+
226
+ - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
227
+
228
+ 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
229
+
230
+ <!-- README_GPTQ.md-text-generation-webui end -->
231
+
232
+ <!-- README_GPTQ.md-use-from-tgi start -->
233
+ ## Serving this model from Text Generation Inference (TGI)
234
+
235
+ It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
236
+
237
+ Example Docker parameters:
238
+
239
+ ```shell
240
+ --model-id TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
241
+ ```
242
+
243
+ Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
244
+
245
+ ```shell
246
+ pip3 install huggingface-hub
247
+ ```
248
+
249
+ ```python
250
+ from huggingface_hub import InferenceClient
251
+
252
+ endpoint_url = "https://your-endpoint-url-here"
253
+
254
+ prompt = "Tell me about AI"
255
+ prompt_template=f'''<|im_start|>system
256
+ {system_message}<|im_end|>
257
+ <|im_start|>user
258
+ {prompt}<|im_end|>
259
+ <|im_start|>assistant
260
+ '''
261
+
262
+ client = InferenceClient(endpoint_url)
263
+ response = client.text_generation(prompt,
264
+ max_new_tokens=128,
265
+ do_sample=True,
266
+ temperature=0.7,
267
+ top_p=0.95,
268
+ top_k=40,
269
+ repetition_penalty=1.1)
270
+
271
+ print(f"Model output: {response}")
272
+ ```
273
+ <!-- README_GPTQ.md-use-from-tgi end -->
274
+ <!-- README_GPTQ.md-use-from-python start -->
275
+ ## Python code example: inference from this GPTQ model
276
+
277
+ ### Install the necessary packages
278
+
279
+ Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
280
+
281
+ ```shell
282
+ pip3 install --upgrade transformers optimum
283
+ # If using PyTorch 2.1 + CUDA 12.x:
284
+ pip3 install --upgrade auto-gptq
285
+ # or, if using PyTorch 2.1 + CUDA 11.x:
286
+ pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
287
+ ```
288
+
289
+ If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
290
+
291
+ ```shell
292
+ pip3 uninstall -y auto-gptq
293
+ git clone https://github.com/PanQiWei/AutoGPTQ
294
+ cd AutoGPTQ
295
+ git checkout v0.5.1
296
+ pip3 install .
297
+ ```
298
+
299
+ ### Example Python code
300
+
301
+ ```python
302
+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
303
+
304
+ model_name_or_path = "TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ"
305
+ # To use a different branch, change revision
306
+ # For example: revision="gptq-4bit-32g-actorder_True"
307
+ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
308
+ device_map="auto",
309
+ trust_remote_code=False,
310
+ revision="main")
311
+
312
+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
313
+
314
+ prompt = "Tell me about AI"
315
+ prompt_template=f'''<|im_start|>system
316
+ {system_message}<|im_end|>
317
+ <|im_start|>user
318
+ {prompt}<|im_end|>
319
+ <|im_start|>assistant
320
+ '''
321
+
322
+ print("\n\n*** Generate:")
323
+
324
+ input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
325
+ output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
326
+ print(tokenizer.decode(output[0]))
327
+
328
+ # Inference can also be done using transformers' pipeline
329
+
330
+ print("*** Pipeline:")
331
+ pipe = pipeline(
332
+ "text-generation",
333
+ model=model,
334
+ tokenizer=tokenizer,
335
+ max_new_tokens=512,
336
+ do_sample=True,
337
+ temperature=0.7,
338
+ top_p=0.95,
339
+ top_k=40,
340
+ repetition_penalty=1.1
341
+ )
342
+
343
+ print(pipe(prompt_template)[0]['generated_text'])
344
+ ```
345
+ <!-- README_GPTQ.md-use-from-python end -->
346
+
347
+ <!-- README_GPTQ.md-compatibility start -->
348
+ ## Compatibility
349
+
350
+ The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
351
+
352
+ [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
353
+
354
+ For a list of clients/servers, please see "Known compatible clients / servers", above.
355
+ <!-- README_GPTQ.md-compatibility end -->
356
+
357
+ <!-- footer start -->
358
+ <!-- 200823 -->
359
+ ## Discord
360
+
361
+ For further support, and discussions on these models and AI in general, join us at:
362
+
363
+ [TheBloke AI's Discord server](https://discord.gg/theblokeai)
364
+
365
+ ## Thanks, and how to contribute
366
+
367
+ Thanks to the [chirper.ai](https://chirper.ai) team!
368
+
369
+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
370
+
371
+ 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.
372
+
373
+ 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.
374
+
375
+ Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
376
+
377
+ * Patreon: https://patreon.com/TheBlokeAI
378
+ * Ko-Fi: https://ko-fi.com/TheBlokeAI
379
+
380
+ **Special thanks to**: Aemon Algiz.
381
+
382
+ **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
383
+
384
+
385
+ Thank you to all my generous patrons and donaters!
386
+
387
+ And thank you again to a16z for their generous grant.
388
+
389
+ <!-- footer end -->
390
+
391
+ # Original model card: Maxime Labonne's NeuralHermes 2.5 Mistral 7B
392
+
393
+
394
+ <center><img src="https://i.imgur.com/qIhaFNM.png"></center>
395
+
396
+ # NeuralHermes 2.5 - Mistral 7B
397
+
398
+ NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
399
+
400
+ It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
401
+
402
+ The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
403
+
404
+ ### Quantized models
405
+ * GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
406
+ * AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
407
+ * EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
408
+
409
+ ## Results
410
+
411
+ Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
412
+
413
+ Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
414
+
415
+ ### AGIEval
416
+ ![](https://i.imgur.com/7an3B1f.png)
417
+
418
+ ### GPT4All
419
+ ![](https://i.imgur.com/TLxZFi9.png)
420
+
421
+ ### TruthfulQA
422
+ ![](https://i.imgur.com/V380MqD.png)
423
+
424
+ You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
425
+
426
+ ## Usage
427
+
428
+ You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
429
+
430
+ You can also run this model using the following code:
431
+
432
+ ```python
433
+ import transformers
434
+ from transformers import AutoTokenizer
435
+
436
+ # Format prompt
437
+ message = [
438
+ {"role": "system", "content": "You are a helpful assistant chatbot."},
439
+ {"role": "user", "content": "What is a Large Language Model?"}
440
+ ]
441
+ tokenizer = AutoTokenizer.from_pretrained(new_model)
442
+ prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
443
+
444
+ # Create pipeline
445
+ pipeline = transformers.pipeline(
446
+ "text-generation",
447
+ model=new_model,
448
+ tokenizer=tokenizer
449
+ )
450
+
451
+ # Generate text
452
+ sequences = pipeline(
453
+ prompt,
454
+ do_sample=True,
455
+ temperature=0.7,
456
+ top_p=0.9,
457
+ num_return_sequences=1,
458
+ max_length=200,
459
+ )
460
+ print(sequences[0]['generated_text'])
461
+ ```
462
+
463
+
464
+ ## Training hyperparameters
465
+
466
+ **LoRA**:
467
+ * r=16
468
+ * lora_alpha=16
469
+ * lora_dropout=0.05
470
+ * bias="none"
471
+ * task_type="CAUSAL_LM"
472
+ * target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
473
+
474
+ **Training arguments**:
475
+ * per_device_train_batch_size=4
476
+ * gradient_accumulation_steps=4
477
+ * gradient_checkpointing=True
478
+ * learning_rate=5e-5
479
+ * lr_scheduler_type="cosine"
480
+ * max_steps=200
481
+ * optim="paged_adamw_32bit"
482
+ * warmup_steps=100
483
+
484
+ **DPOTrainer**:
485
+ * beta=0.1
486
+ * max_prompt_length=1024
487
+ * max_length=1536