Instructions to use HF1BitLLM/Llama3-8B-1.58-100B-tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HF1BitLLM/Llama3-8B-1.58-100B-tokens with Transformers:

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

pipe = pipeline("text-generation", model="HF1BitLLM/Llama3-8B-1.58-100B-tokens")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HF1BitLLM/Llama3-8B-1.58-100B-tokens")
model = AutoModelForCausalLM.from_pretrained("HF1BitLLM/Llama3-8B-1.58-100B-tokens", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HF1BitLLM/Llama3-8B-1.58-100B-tokens with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HF1BitLLM/Llama3-8B-1.58-100B-tokens"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HF1BitLLM/Llama3-8B-1.58-100B-tokens",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HF1BitLLM/Llama3-8B-1.58-100B-tokens

SGLang

How to use HF1BitLLM/Llama3-8B-1.58-100B-tokens 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 "HF1BitLLM/Llama3-8B-1.58-100B-tokens" \
    --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": "HF1BitLLM/Llama3-8B-1.58-100B-tokens",
		"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 "HF1BitLLM/Llama3-8B-1.58-100B-tokens" \
        --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": "HF1BitLLM/Llama3-8B-1.58-100B-tokens",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HF1BitLLM/Llama3-8B-1.58-100B-tokens with Docker Model Runner:
```
docker model run hf.co/HF1BitLLM/Llama3-8B-1.58-100B-tokens
```

You can try to convert DeepSeek-V2.5 or Llama-3.1-Nemotron-70B-Instruct-HF?

by win10 - opened Oct 19, 2024

Discussion

win10

Oct 19, 2024

You can try to convert
DeepSeek-V2.5 or
Llama-3.1-Nemotron-70B-Instruct-HF?

Ttimofeyka

Oct 19, 2024

We with my friend wanna do this with LLama-3.1-70B-Instruct (we have resources to finetune), but I don't know how to quantize 70B model to 1.58 bit.
@medmekk do you have a some script or anything to convert a model from llama arch to llamabitnet arch? I mean, efficient quantization of linear layers.

medmekk

Hugging Face 1Bit LLMs org Oct 21, 2024

To convert a model from llama arch to llama bitnet you can just check this PR : https://github.com/huggingface/nanotron/pull/180/files, check how the linears are defined TensorParallelColumnLinearBitNet and TensorParallelRowLinearBitNet (don't worry about the two linear row & column types, it has to do with Tensor parallelism) with the fake quantization. Also you can check this handbook from microsoft it helped me a lot : https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment