Commit
•
f01df9e
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Parent(s):
Duplicate from hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4
Browse filesCo-authored-by: Alvaro Bartolome <alvarobartt@users.noreply.huggingface.co>
- .gitattributes +35 -0
- LICENSE +114 -0
- README.md +300 -0
- USE_POLICY.md +51 -0
- config.json +46 -0
- generation_config.json +12 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +746 -0
- special_tokens_map.json +16 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2062 -0
.gitattributes
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LICENSE
ADDED
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| 1 |
+
LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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Llama 3.1 Version Release Date: July 23, 2024
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
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Llama Materials set forth herein.
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“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1
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distributed by Meta at https://llama.meta.com/doc/overview.
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“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into
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“Llama 3.1” means the foundational large language models and software and algorithms, including
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https://llama.meta.com/llama-downloads.
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“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any
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README.md
ADDED
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|
|
| 1 |
+
---
|
| 2 |
+
license: llama3.1
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- de
|
| 6 |
+
- fr
|
| 7 |
+
- 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-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) which is the BF16 half-precision official version released by Meta AI.
|
| 22 |
+
|
| 23 |
+
## Model Information
|
| 24 |
+
|
| 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, 405B) 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-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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 8B Instruct AWQ in INT4, around 4 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 8B 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 8B 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-3.1-8B-Instruct-AWQ-INT4"
|
| 51 |
+
|
| 52 |
+
quantization_config = AwqConfig(
|
| 53 |
+
bits=4,
|
| 54 |
+
fuse_max_seq_len=512, # Note: Update this as per your use-case
|
| 55 |
+
do_fuse=True,
|
| 56 |
+
)
|
| 57 |
+
|
| 58 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
| 59 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 60 |
+
model_id,
|
| 61 |
+
torch_dtype=torch.float16,
|
| 62 |
+
low_cpu_mem_usage=True,
|
| 63 |
+
device_map="auto",
|
| 64 |
+
quantization_config=quantization_config
|
| 65 |
+
)
|
| 66 |
+
|
| 67 |
+
prompt = [
|
| 68 |
+
{"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
|
| 69 |
+
{"role": "user", "content": "What's Deep Learning?"},
|
| 70 |
+
]
|
| 71 |
+
inputs = tokenizer.apply_chat_template(
|
| 72 |
+
prompt,
|
| 73 |
+
tokenize=True,
|
| 74 |
+
add_generation_prompt=True,
|
| 75 |
+
return_tensors="pt",
|
| 76 |
+
return_dict=True,
|
| 77 |
+
).to("cuda")
|
| 78 |
+
|
| 79 |
+
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
|
| 80 |
+
print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
|
| 81 |
+
```
|
| 82 |
+
|
| 83 |
+
### AutoAWQ
|
| 84 |
+
|
| 85 |
+
In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, you need to install the following packages:
|
| 86 |
+
|
| 87 |
+
```bash
|
| 88 |
+
pip install -q --upgrade transformers autoawq accelerate
|
| 89 |
+
```
|
| 90 |
+
|
| 91 |
+
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.
|
| 92 |
+
|
| 93 |
+
```python
|
| 94 |
+
import torch
|
| 95 |
+
from awq import AutoAWQForCausalLM
|
| 96 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 97 |
+
|
| 98 |
+
model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
|
| 99 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
| 100 |
+
model = AutoAWQForCausalLM.from_pretrained(
|
| 101 |
+
model_id,
|
| 102 |
+
torch_dtype=torch.float16,
|
| 103 |
+
low_cpu_mem_usage=True,
|
| 104 |
+
device_map="auto",
|
| 105 |
+
)
|
| 106 |
+
|
| 107 |
+
prompt = [
|
| 108 |
+
{"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
|
| 109 |
+
{"role": "user", "content": "What's Deep Learning?"},
|
| 110 |
+
]
|
| 111 |
+
inputs = tokenizer.apply_chat_template(
|
| 112 |
+
prompt,
|
| 113 |
+
tokenize=True,
|
| 114 |
+
add_generation_prompt=True,
|
| 115 |
+
return_tensors="pt",
|
| 116 |
+
return_dict=True,
|
| 117 |
+
).to("cuda")
|
| 118 |
+
|
| 119 |
+
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
|
| 120 |
+
print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
|
| 121 |
+
```
|
| 122 |
+
|
| 123 |
+
The AutoAWQ script has been adapted from [`AutoAWQ/examples/generate.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py).
|
| 124 |
+
|
| 125 |
+
### 🤗 Text Generation Inference (TGI)
|
| 126 |
+
|
| 127 |
+
To run the `text-generation-launcher` with Llama 3.1 8B 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.
|
| 128 |
+
|
| 129 |
+
```bash
|
| 130 |
+
pip install -q --upgrade huggingface_hub
|
| 131 |
+
huggingface-cli login
|
| 132 |
+
```
|
| 133 |
+
|
| 134 |
+
Then you just need to run the TGI v2.2.0 (or higher) Docker container as follows:
|
| 135 |
+
|
| 136 |
+
```bash
|
| 137 |
+
docker run --gpus all --shm-size 1g -ti -p 8080:80 \
|
| 138 |
+
-v hf_cache:/data \
|
| 139 |
+
-e MODEL_ID=hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
|
| 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-3.1-8B-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 8B 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-3.1-8B-Instruct-AWQ-INT4 \
|
| 217 |
+
--max-model-len 4096
|
| 218 |
+
```
|
| 219 |
+
|
| 220 |
+
To send request to the deployed vLLM endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`:
|
| 221 |
+
|
| 222 |
+
```bash
|
| 223 |
+
curl 0.0.0.0:8000/v1/chat/completions \
|
| 224 |
+
-X POST \
|
| 225 |
+
-H 'Content-Type: application/json' \
|
| 226 |
+
-d '{
|
| 227 |
+
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
| 228 |
+
"messages": [
|
| 229 |
+
{
|
| 230 |
+
"role": "system",
|
| 231 |
+
"content": "You are a helpful assistant."
|
| 232 |
+
},
|
| 233 |
+
{
|
| 234 |
+
"role": "user",
|
| 235 |
+
"content": "What is Deep Learning?"
|
| 236 |
+
}
|
| 237 |
+
],
|
| 238 |
+
"max_tokens": 128
|
| 239 |
+
}'
|
| 240 |
+
```
|
| 241 |
+
|
| 242 |
+
Or programatically via the `openai` Python client (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows:
|
| 243 |
+
|
| 244 |
+
```python
|
| 245 |
+
import os
|
| 246 |
+
from openai import OpenAI
|
| 247 |
+
|
| 248 |
+
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key=os.getenv("VLLM_API_KEY", "-"))
|
| 249 |
+
|
| 250 |
+
chat_completion = client.chat.completions.create(
|
| 251 |
+
model="hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
|
| 252 |
+
messages=[
|
| 253 |
+
{"role": "system", "content": "You are a helpful assistant."},
|
| 254 |
+
{"role": "user", "content": "What is Deep Learning?"},
|
| 255 |
+
],
|
| 256 |
+
max_tokens=128,
|
| 257 |
+
)
|
| 258 |
+
```
|
| 259 |
+
|
| 260 |
+
## Quantization Reproduction
|
| 261 |
+
|
| 262 |
+
> [!NOTE]
|
| 263 |
+
> In order to quantize Llama 3.1 8B Instruct using AutoAWQ, you will need to use an instance with at least enough CPU RAM to fit the whole model i.e. ~8GiB, and an NVIDIA GPU with 16GiB of VRAM to quantize it.
|
| 264 |
+
|
| 265 |
+
In order to quantize Llama 3.1 8B Instruct, first install the following packages:
|
| 266 |
+
|
| 267 |
+
```bash
|
| 268 |
+
pip install -q --upgrade transformers autoawq accelerate
|
| 269 |
+
```
|
| 270 |
+
|
| 271 |
+
Then run the following script, adapted from [`AutoAWQ/examples/quantize.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py):
|
| 272 |
+
|
| 273 |
+
```python
|
| 274 |
+
from awq import AutoAWQForCausalLM
|
| 275 |
+
from transformers import AutoTokenizer
|
| 276 |
+
|
| 277 |
+
model_path = "meta-llama/Meta-Llama-3.1-8B-Instruct"
|
| 278 |
+
quant_path = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
|
| 279 |
+
quant_config = {
|
| 280 |
+
"zero_point": True,
|
| 281 |
+
"q_group_size": 128,
|
| 282 |
+
"w_bit": 4,
|
| 283 |
+
"version": "GEMM",
|
| 284 |
+
}
|
| 285 |
+
|
| 286 |
+
# Load model
|
| 287 |
+
model = AutoAWQForCausalLM.from_pretrained(
|
| 288 |
+
model_path, low_cpu_mem_usage=True, use_cache=False,
|
| 289 |
+
)
|
| 290 |
+
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
| 291 |
+
|
| 292 |
+
# Quantize
|
| 293 |
+
model.quantize(tokenizer, quant_config=quant_config)
|
| 294 |
+
|
| 295 |
+
# Save quantized model
|
| 296 |
+
model.save_quantized(quant_path)
|
| 297 |
+
tokenizer.save_pretrained(quant_path)
|
| 298 |
+
|
| 299 |
+
print(f'Model is quantized and saved at "{quant_path}"')
|
| 300 |
+
```
|
USE_POLICY.md
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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
|
config.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"LlamaForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 128000,
|
| 8 |
+
"eos_token_id": [
|
| 9 |
+
128001,
|
| 10 |
+
128008,
|
| 11 |
+
128009
|
| 12 |
+
],
|
| 13 |
+
"hidden_act": "silu",
|
| 14 |
+
"hidden_size": 4096,
|
| 15 |
+
"initializer_range": 0.02,
|
| 16 |
+
"intermediate_size": 14336,
|
| 17 |
+
"max_position_embeddings": 131072,
|
| 18 |
+
"mlp_bias": false,
|
| 19 |
+
"model_type": "llama",
|
| 20 |
+
"num_attention_heads": 32,
|
| 21 |
+
"num_hidden_layers": 32,
|
| 22 |
+
"num_key_value_heads": 8,
|
| 23 |
+
"pretraining_tp": 1,
|
| 24 |
+
"quantization_config": {
|
| 25 |
+
"bits": 4,
|
| 26 |
+
"group_size": 128,
|
| 27 |
+
"modules_to_not_convert": null,
|
| 28 |
+
"quant_method": "awq",
|
| 29 |
+
"version": "gemm",
|
| 30 |
+
"zero_point": true
|
| 31 |
+
},
|
| 32 |
+
"rms_norm_eps": 1e-05,
|
| 33 |
+
"rope_scaling": {
|
| 34 |
+
"factor": 8.0,
|
| 35 |
+
"low_freq_factor": 1.0,
|
| 36 |
+
"high_freq_factor": 4.0,
|
| 37 |
+
"original_max_position_embeddings": 8192,
|
| 38 |
+
"rope_type": "llama3"
|
| 39 |
+
},
|
| 40 |
+
"rope_theta": 500000.0,
|
| 41 |
+
"tie_word_embeddings": false,
|
| 42 |
+
"torch_dtype": "float16",
|
| 43 |
+
"transformers_version": "4.43.0.dev0",
|
| 44 |
+
"use_cache": false,
|
| 45 |
+
"vocab_size": 128256
|
| 46 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 128000,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
128001,
|
| 6 |
+
128008,
|
| 7 |
+
128009
|
| 8 |
+
],
|
| 9 |
+
"temperature": 0.6,
|
| 10 |
+
"top_p": 0.9,
|
| 11 |
+
"transformers_version": "4.43.0.dev0"
|
| 12 |
+
}
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e152d461dbe23f8e7606edbd1cc859a2cdc85a46766d8cb6a629eb88832c5718
|
| 3 |
+
size 4677265296
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
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|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3e9a36ad418b0cd9b253f65771552d7dc05cf1abf1170b94de4b4d546aac255d
|
| 3 |
+
size 1050673280
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,746 @@
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|
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|
|
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|
|
|
|
|
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|
|
|
|
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,16 @@
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| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
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"content": "<|eot_id|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
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"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
}
|
| 16 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,2062 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|finetune_right_pad_id|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_2|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|eom_id|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|python_tag|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_3|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|reserved_special_token_4|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|reserved_special_token_5|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
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|
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|
| 1777 |
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"content": "<|reserved_special_token_214|>",
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| 1781 |
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|
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|
| 1784 |
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"single_word": false,
|
| 1785 |
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|
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|
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|
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|
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"single_word": false,
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"special": true
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| 1836 |
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|
| 1840 |
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|
| 1841 |
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"special": true
|
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},
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|
| 1844 |
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| 1845 |
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|
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|
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|
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"single_word": false,
|
| 1857 |
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"special": true
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},
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"128232": {
|
| 1860 |
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"content": "<|reserved_special_token_224|>",
|
| 1861 |
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|
| 1862 |
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"single_word": false,
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| 1865 |
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"special": true
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|
| 1884 |
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"content": "<|reserved_special_token_227|>",
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| 1885 |
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|
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"special": true
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"content": "<|reserved_special_token_228|>",
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|
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},
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|
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|
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"single_word": false,
|
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"special": true
|
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},
|
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|
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},
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"128250": {
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"normalized": false,
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"rstrip": false,
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"single_word": false,
|
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"special": true
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},
|
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"128251": {
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"content": "<|reserved_special_token_243|>",
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"lstrip": false,
|
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"normalized": false,
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"rstrip": false,
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"single_word": false,
|
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"special": true
|
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},
|
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"128252": {
|
| 2020 |
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"content": "<|reserved_special_token_244|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
|
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"single_word": false,
|
| 2025 |
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"special": true
|
| 2026 |
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},
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"128253": {
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| 2028 |
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"content": "<|reserved_special_token_245|>",
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| 2029 |
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"lstrip": false,
|
| 2030 |
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"normalized": false,
|
| 2031 |
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"rstrip": false,
|
| 2032 |
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"single_word": false,
|
| 2033 |
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"special": true
|
| 2034 |
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},
|
| 2035 |
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"128254": {
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"content": "<|reserved_special_token_246|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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| 2040 |
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"single_word": false,
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| 2041 |
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"special": true
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},
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"128255": {
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"content": "<|reserved_special_token_247|>",
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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| 2049 |
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"special": true
|
| 2050 |
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}
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| 2051 |
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},
|
| 2052 |
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"bos_token": "<|begin_of_text|>",
|
| 2053 |
+
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
|
| 2054 |
+
"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 |
+
}
|