[!IMPORTANT] This repository is a community-driven quantized version of the original model
meta-llama/Meta-Llama-3.1-8B-Instructwhich is the BF16 half-precision official version released by Meta AI.
Model Information
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.
This repository contains meta-llama/Meta-Llama-3.1-8B-Instruct quantized using AutoAWQ from FP16 down to INT4 using the GEMM kernels performing zero-point quantization with a group size of 128.
Model Usage
[!NOTE] 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.
In order to use the current quantized model, support is offered for different solutions as transformers, autoawq, or text-generation-inference.
🤗 Transformers
In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, you need to install the following packages:
pip install -q --upgrade transformers autoawq accelerate
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.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=512, # Note: Update this as per your use-case
do_fuse=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto",
quantization_config=quantization_config
)
prompt = [
{"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
{"role": "user", "content": "What's Deep Learning?"},
]
inputs = tokenizer.apply_chat_template(
prompt,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
AutoAWQ
In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, you need to install the following packages:
pip install -q --upgrade transformers autoawq accelerate
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.
import torch
from awq import AutoAWQForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoAWQForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto",
)
prompt = [
{"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
{"role": "user", "content": "What's Deep Learning?"},
]
inputs = tokenizer.apply_chat_template(
prompt,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
The AutoAWQ script has been adapted from AutoAWQ/examples/generate.py.
🤗 Text Generation Inference (TGI)
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) and the huggingface_hub Python package as you need to login to the Hugging Face Hub.
pip install -q --upgrade huggingface_hub
huggingface-cli login
Then you just need to run the TGI v2.2.0 (or higher) Docker container as follows:
docker run --gpus all --shm-size 1g -ti -p 8080:80 \
-v hf_cache:/data \
-e MODEL_ID=hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
-e QUANTIZE=awq \
-e HF_TOKEN=$(cat ~/.cache/huggingface/token) \
-e MAX_INPUT_LENGTH=4000 \
-e MAX_TOTAL_TOKENS=4096 \
ghcr.io/huggingface/text-generation-inference:2.2.0
[!NOTE] TGI will expose different endpoints, to see all the endpoints available check TGI OpenAPI Specification.
To send request to the deployed TGI endpoint compatible with OpenAI OpenAPI specification i.e. /v1/chat/completions:
curl 0.0.0.0:8080/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What is Deep Learning?"
}
],
"max_tokens": 128
}'
Or programatically via the huggingface_hub Python client as follows:
import os
from huggingface_hub import InferenceClient
client = InferenceClient(base_url="http://0.0.0.0:8080", api_key=os.getenv("HF_TOKEN", "-"))
chat_completion = client.chat.completions.create(
model="hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Deep Learning?"},
],
max_tokens=128,
)
Alternatively, the OpenAI Python client can also be used (see installation notes) as follows:
import os
from openai import OpenAI
client = OpenAI(base_url="http://0.0.0.0:8080/v1", api_key=os.getenv("OPENAI_API_KEY", "-"))
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Deep Learning?"},
],
max_tokens=128,
)
vLLM
To run vLLM with Llama 3.1 8B Instruct AWQ in INT4, you will need to have Docker installed (see installation notes) and run the latest vLLM Docker container as follows:
docker run --runtime nvidia --gpus all --ipc=host -p 8000:8000 \
-v hf_cache:/root/.cache/huggingface \
vllm/vllm-openai:latest \
--model hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
--max-model-len 4096
To send request to the deployed vLLM endpoint compatible with OpenAI OpenAPI specification i.e. /v1/chat/completions:
curl 0.0.0.0:8000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What is Deep Learning?"
}
],
"max_tokens": 128
}'
Or programatically via the openai Python client (see installation notes) as follows:
import os
from openai import OpenAI
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key=os.getenv("VLLM_API_KEY", "-"))
chat_completion = client.chat.completions.create(
model="hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Deep Learning?"},
],
max_tokens=128,
)
Quantization Reproduction
[!NOTE] 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.
In order to quantize Llama 3.1 8B Instruct, first install the following packages:
pip install -q --upgrade transformers autoawq accelerate
Then run the following script, adapted from AutoAWQ/examples/quantize.py:
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_path = "meta-llama/Meta-Llama-3.1-8B-Instruct"
quant_path = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
quant_config = {
"zero_point": True,
"q_group_size": 128,
"w_bit": 4,
"version": "GEMM",
}
# Load model
model = AutoAWQForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, use_cache=False,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Quantize
model.quantize(tokenizer, quant_config=quant_config)
# Save quantized model
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)
print(f'Model is quantized and saved at "{quant_path}"')