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Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Text Generation
Transformers
Safetensors
llama
llama-3.1
meta
autoawq
conversational
text-generation-inference
Inference Endpoints
4-bit precision
awq
Model card Files Community
jburmeister alvarobartt HF staff commited on
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Duplicate from hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

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Co-authored-by: Alvaro Bartolome <alvarobartt@users.noreply.huggingface.co>

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  1. .gitattributes +35 -0
  2. LICENSE +114 -0
  3. README.md +301 -0
  4. USE_POLICY.md +51 -0
  5. config.json +47 -0
  6. generation_config.json +12 -0
  7. model-00001-of-00009.safetensors +3 -0
  8. model-00002-of-00009.safetensors +3 -0
  9. model-00003-of-00009.safetensors +3 -0
  10. model-00004-of-00009.safetensors +3 -0
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  12. model-00006-of-00009.safetensors +3 -0
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  14. model-00008-of-00009.safetensors +3 -0
  15. model-00009-of-00009.safetensors +3 -0
  16. model.safetensors.index.json +0 -0
  17. special_tokens_map.json +16 -0
  18. tokenizer.json +0 -0
  19. tokenizer_config.json +2062 -0
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LICENSE ADDED
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+ LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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README.md ADDED
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1
+ ---
2
+ license: llama3.1
3
+ language:
4
+ - en
5
+ - de
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+ - fr
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+ - it
8
+ - pt
9
+ - hi
10
+ - es
11
+ - th
12
+ library_name: transformers
13
+ pipeline_tag: text-generation
14
+ tags:
15
+ - llama-3.1
16
+ - meta
17
+ - autoawq
18
+ ---
19
+
20
+ > [!IMPORTANT]
21
+ > This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) which is the FP16 half-precision official version released by Meta AI.
22
+
23
+ ## Model Information
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+
25
+ The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 70B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
26
+
27
+ This repository contains [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) quantized using [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) from FP16 down to INT4 using the GEMM kernels performing zero-point quantization with a group size of 128.
28
+
29
+ ## Model Usage
30
+
31
+ > [!NOTE]
32
+ > In order to run the inference with Llama 3.1 70B Instruct AWQ in INT4, around 35 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available.
33
+
34
+ In order to use the current quantized model, support is offered for different solutions as `transformers`, `autoawq`, or `text-generation-inference`.
35
+
36
+ ### 🤗 Transformers
37
+
38
+ In order to run the inference with Llama 3.1 70B Instruct AWQ in INT4, you need to install the following packages:
39
+
40
+ ```bash
41
+ pip install -q --upgrade transformers autoawq accelerate
42
+ ```
43
+
44
+ To run the inference on top of Llama 3.1 70B Instruct AWQ in INT4 precision, the AWQ model can be instantiated as any other causal language modeling model via `AutoModelForCausalLM` and run the inference normally.
45
+
46
+ ```python
47
+ import torch
48
+ from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
49
+
50
+ model_id = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4"
51
+ quantization_config = AwqConfig(
52
+ bits=4,
53
+ fuse_max_seq_len=512, # Note: Update this as per your use-case
54
+ do_fuse=True,
55
+ )
56
+
57
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
58
+ model = AutoModelForCausalLM.from_pretrained(
59
+ model_id,
60
+ torch_dtype=torch.float16,
61
+ low_cpu_mem_usage=True,
62
+ device_map="auto",
63
+ quantization_config=quantization_config
64
+ )
65
+
66
+ prompt = [
67
+ {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
68
+ {"role": "user", "content": "What's Deep Learning?"},
69
+ ]
70
+ inputs = tokenizer.apply_chat_template(
71
+ prompt,
72
+ tokenize=True,
73
+ add_generation_prompt=True,
74
+ return_tensors="pt",
75
+ return_dict=True,
76
+ ).to("cuda")
77
+
78
+ outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
79
+ print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
80
+ ```
81
+
82
+ ### AutoAWQ
83
+
84
+ In order to run the inference with Llama 3.1 70B Instruct AWQ in INT4, you need to install the following packages:
85
+
86
+ ```bash
87
+ pip install -q --upgrade transformers autoawq accelerate
88
+ ```
89
+
90
+ Alternatively, one may want to run that via `AutoAWQ` even though it's built on top of 🤗 `transformers`, which is the recommended approach instead as described above.
91
+
92
+ ```python
93
+ import torch
94
+ from awq import AutoAWQForCausalLM
95
+ from transformers import AutoModelForCausalLM, AutoTokenizer
96
+
97
+ model_id = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4"
98
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
99
+ model = AutoAWQForCausalLM.from_pretrained(
100
+ model_id,
101
+ torch_dtype=torch.float16,
102
+ low_cpu_mem_usage=True,
103
+ device_map="auto",
104
+ )
105
+
106
+ prompt = [
107
+ {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
108
+ {"role": "user", "content": "What's Deep Learning?"},
109
+ ]
110
+ inputs = tokenizer.apply_chat_template(
111
+ prompt,
112
+ tokenize=True,
113
+ add_generation_prompt=True,
114
+ return_tensors="pt",
115
+ return_dict=True,
116
+ ).to("cuda")
117
+
118
+ outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
119
+ print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
120
+ ```
121
+
122
+ The AutoAWQ script has been adapted from [AutoAWQ/examples/generate.py](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py).
123
+
124
+ ### 🤗 Text Generation Inference (TGI)
125
+
126
+ To run the `text-generation-launcher` with Llama 3.1 70B Instruct AWQ in INT4 with Marlin kernels for optimized inference speed, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)) and the `huggingface_hub` Python package as you need to login to the Hugging Face Hub.
127
+
128
+ ```bash
129
+ pip install -q --upgrade huggingface_hub
130
+ huggingface-cli login
131
+ ```
132
+
133
+ Then you just need to run the TGI v2.2.0 (or higher) Docker container as follows:
134
+
135
+ ```bash
136
+ docker run --gpus all --shm-size 1g -ti -p 8080:80 \
137
+ -v hf_cache:/data \
138
+ -e MODEL_ID=hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 \
139
+ -e NUM_SHARD=4 \
140
+ -e QUANTIZE=awq \
141
+ -e HF_TOKEN=$(cat ~/.cache/huggingface/token) \
142
+ -e MAX_INPUT_LENGTH=4000 \
143
+ -e MAX_TOTAL_TOKENS=4096 \
144
+ ghcr.io/huggingface/text-generation-inference:2.2.0
145
+ ```
146
+
147
+ > [!NOTE]
148
+ > TGI will expose different endpoints, to see all the endpoints available check [TGI OpenAPI Specification](https://huggingface.github.io/text-generation-inference/#/).
149
+
150
+ To send request to the deployed TGI endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`:
151
+
152
+ ```bash
153
+ curl 0.0.0.0:8080/v1/chat/completions \
154
+ -X POST \
155
+ -H 'Content-Type: application/json' \
156
+ -d '{
157
+ "model": "tgi",
158
+ "messages": [
159
+ {
160
+ "role": "system",
161
+ "content": "You are a helpful assistant."
162
+ },
163
+ {
164
+ "role": "user",
165
+ "content": "What is Deep Learning?"
166
+ }
167
+ ],
168
+ "max_tokens": 128
169
+ }'
170
+ ```
171
+
172
+ Or programatically via the `huggingface_hub` Python client as follows:
173
+
174
+ ```python
175
+ import os
176
+ from huggingface_hub import InferenceClient
177
+
178
+ client = InferenceClient(base_url="http://0.0.0.0:8080", api_key=os.getenv("HF_TOKEN", "-"))
179
+
180
+ chat_completion = client.chat.completions.create(
181
+ model="hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4",
182
+ messages=[
183
+ {"role": "system", "content": "You are a helpful assistant."},
184
+ {"role": "user", "content": "What is Deep Learning?"},
185
+ ],
186
+ max_tokens=128,
187
+ )
188
+ ```
189
+
190
+ Alternatively, the OpenAI Python client can also be used (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows:
191
+
192
+ ```python
193
+ import os
194
+ from openai import OpenAI
195
+
196
+ client = OpenAI(base_url="http://0.0.0.0:8080/v1", api_key=os.getenv("OPENAI_API_KEY", "-"))
197
+
198
+ chat_completion = client.chat.completions.create(
199
+ model="tgi",
200
+ messages=[
201
+ {"role": "system", "content": "You are a helpful assistant."},
202
+ {"role": "user", "content": "What is Deep Learning?"},
203
+ ],
204
+ max_tokens=128,
205
+ )
206
+ ```
207
+
208
+ ### vLLM
209
+
210
+ To run vLLM with Llama 3.1 70B Instruct AWQ in INT4, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)) and run the latest vLLM Docker container as follows:
211
+
212
+ ```bash
213
+ docker run --runtime nvidia --gpus all --ipc=host -p 8000:8000 \
214
+ -v hf_cache:/root/.cache/huggingface \
215
+ vllm/vllm-openai:latest \
216
+ --model hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 \
217
+ --tensor-parallel-size 4 \
218
+ --max-model-len 4096
219
+ ```
220
+
221
+ To send request to the deployed vLLM endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`:
222
+
223
+ ```bash
224
+ curl 0.0.0.0:8000/v1/chat/completions \
225
+ -X POST \
226
+ -H 'Content-Type: application/json' \
227
+ -d '{
228
+ "model": "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4",
229
+ "messages": [
230
+ {
231
+ "role": "system",
232
+ "content": "You are a helpful assistant."
233
+ },
234
+ {
235
+ "role": "user",
236
+ "content": "What is Deep Learning?"
237
+ }
238
+ ],
239
+ "max_tokens": 128
240
+ }'
241
+ ```
242
+
243
+ Or programatically via the `openai` Python client (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows:
244
+
245
+ ```python
246
+ import os
247
+ from openai import OpenAI
248
+
249
+ client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key=os.getenv("VLLM_API_KEY", "-"))
250
+
251
+ chat_completion = client.chat.completions.create(
252
+ model="hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4",
253
+ messages=[
254
+ {"role": "system", "content": "You are a helpful assistant."},
255
+ {"role": "user", "content": "What is Deep Learning?"},
256
+ ],
257
+ max_tokens=128,
258
+ )
259
+ ```
260
+
261
+ ## Quantization Reproduction
262
+
263
+ > [!NOTE]
264
+ > In order to quantize Llama 3.1 70B Instruct using AutoAWQ, you will need to use an instance with at least enough CPU RAM to fit the whole model i.e. ~140GiB, and an NVIDIA GPU with 40GiB of VRAM to quantize it.
265
+
266
+ In order to quantize Llama 3.1 70B Instruct, first install the following packages:
267
+
268
+ ```bash
269
+ pip install -q --upgrade transformers autoawq accelerate
270
+ ```
271
+
272
+ Then run the following script, adapted from [`AutoAWQ/examples/quantize.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py):
273
+
274
+ ```python
275
+ from awq import AutoAWQForCausalLM
276
+ from transformers import AutoTokenizer
277
+
278
+ model_path = "meta-llama/Meta-Llama-3.1-70B-Instruct"
279
+ quant_path = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4"
280
+ quant_config = {
281
+ "zero_point": True,
282
+ "q_group_size": 128,
283
+ "w_bit": 4,
284
+ "version": "GEMM",
285
+ }
286
+
287
+ # Load model
288
+ model = AutoAWQForCausalLM.from_pretrained(
289
+ model_path, low_cpu_mem_usage=True, use_cache=False,
290
+ )
291
+ tokenizer = AutoTokenizer.from_pretrained(model_path)
292
+
293
+ # Quantize
294
+ model.quantize(tokenizer, quant_config=quant_config)
295
+
296
+ # Save quantized model
297
+ model.save_quantized(quant_path)
298
+ tokenizer.save_pretrained(quant_path)
299
+
300
+ print(f'Model is quantized and saved at "{quant_path}"')
301
+ ```
USE_POLICY.md ADDED
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1
+ # Llama 3.1 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you
4
+ access or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
5
+ this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
6
+
7
+ ## Prohibited Uses
8
+
9
+ We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow
10
+ others to use, Llama 3.1 to:
11
+
12
+ 1. Violate the law or others’ rights, including to:
13
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
14
+ 1. Violence or terrorism
15
+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
16
+ 3. Human trafficking, exploitation, and sexual violence
17
+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
18
+ 5. Sexual solicitation
19
+ 6. Any other criminal activity
20
+ 3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
21
+ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
22
+ 5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
23
+ 6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
24
+ 7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
25
+ 8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
26
+
27
+ 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:
28
+ 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
29
+ 2. Guns and illegal weapons (including weapon development)
30
+ 3. Illegal drugs and regulated/controlled substances
31
+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
32
+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
33
+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
34
+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:
36
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
37
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
38
+ 3. Generating, promoting, or further distributing spam
39
+ 4. Impersonating another individual without consent, authorization, or legal right
40
+ 5. Representing that the use of Llama 3.1 or outputs are human-generated
41
+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
42
+
43
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
44
+
45
+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
46
+ of this Policy through one of the following means:
47
+
48
+ * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
49
+ * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
50
+ * Reporting bugs and security concerns: facebook.com/whitehat/info
51
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com
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+ {
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+ "_name_or_path": "/fsx/alvaro.bartolome/70b-instruct",
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+ "architectures": [
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+ "hidden_size": 8192,
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+ "intermediate_size": 28672,
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+ "mlp_bias": false,
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+ "num_attention_heads": 64,
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+ "num_hidden_layers": 80,
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+ "modules_to_not_convert": null,
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+ "quant_method": "awq",
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+ "rms_norm_eps": 1e-05,
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+ "rope_type": "llama3"
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+ "rope_theta": 500000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.43.0.dev0",
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+ "use_cache": false,
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+ "vocab_size": 128256
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+ }
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+ "transformers_version": "4.43.0.dev0"
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+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }