Instructions to use dahara1/llama3.1-8b-Instruct-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dahara1/llama3.1-8b-Instruct-awq with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dahara1/llama3.1-8b-Instruct-awq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dahara1/llama3.1-8b-Instruct-awq")
model = AutoModelForCausalLM.from_pretrained("dahara1/llama3.1-8b-Instruct-awq", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dahara1/llama3.1-8b-Instruct-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dahara1/llama3.1-8b-Instruct-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dahara1/llama3.1-8b-Instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dahara1/llama3.1-8b-Instruct-awq

SGLang

How to use dahara1/llama3.1-8b-Instruct-awq with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dahara1/llama3.1-8b-Instruct-awq" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dahara1/llama3.1-8b-Instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dahara1/llama3.1-8b-Instruct-awq" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dahara1/llama3.1-8b-Instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dahara1/llama3.1-8b-Instruct-awq with Docker Model Runner:
```
docker model run hf.co/dahara1/llama3.1-8b-Instruct-awq
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

llama3.1-8bのAWQ量子化版です。
4GB超のGPUメモリがあれば高速に動かす事ができます。

This is the AWQ quantization version of llama3.1-8b.
If you have more than 4GB of GPU memory, you can run it at high speed.　　

量子化時に日本語と中国語を多めに使っているため、hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4より日本語データを使って計測したPerplexityが良い事がわかっています
Because Japanese and Chinese are used a lot during quantization, It is known that Perplexity measured using Japanese data is better than hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4.

セットアップ(setup)

pip install transformers==4.43.3 autoawq==0.2.6 accelerate==0.33.0

サンプルスクリプト(sample script)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig

model_id = "dahara1/llama3.1-8b-Instruct-awq"

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": "あなたは親切で役に立つアシスタントです。常に海賊のように返答してください"},
  {"role": "user", "content": "ディープラーニングとは何ですか？"},
]
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])

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Safetensors

Model size

8B params

Tensor type

I32

F16