Instructions to use ejbejaranos/Bitnet-Llama3-from8BM-now2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ejbejaranos/Bitnet-Llama3-from8BM-now2B with Transformers:

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

pipe = pipeline("text-generation", model="ejbejaranos/Bitnet-Llama3-from8BM-now2B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ejbejaranos/Bitnet-Llama3-from8BM-now2B")
model = AutoModelForCausalLM.from_pretrained("ejbejaranos/Bitnet-Llama3-from8BM-now2B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ejbejaranos/Bitnet-Llama3-from8BM-now2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ejbejaranos/Bitnet-Llama3-from8BM-now2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ejbejaranos/Bitnet-Llama3-from8BM-now2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ejbejaranos/Bitnet-Llama3-from8BM-now2B

SGLang

How to use ejbejaranos/Bitnet-Llama3-from8BM-now2B 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 "ejbejaranos/Bitnet-Llama3-from8BM-now2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ejbejaranos/Bitnet-Llama3-from8BM-now2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ejbejaranos/Bitnet-Llama3-from8BM-now2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ejbejaranos/Bitnet-Llama3-from8BM-now2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ejbejaranos/Bitnet-Llama3-from8BM-now2B with Docker Model Runner:
```
docker model run hf.co/ejbejaranos/Bitnet-Llama3-from8BM-now2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🚀 BitNet-Llama3 (from 8B to 2B) Transformation & Training

This project transforms a Llama3 model from 8B parameters to a BitNet architecture with 2B parameters, applying BitLinear layers. Additionally, the model is trained with a predefined dataset and uploaded to Hugging Face for future use.

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: ejbejaranos@gmail.com
Funded by [optional]: ITCL
Shared by [optional]: [More Information Needed]
Model type: LLama3 8B Tramsformed to Bitnet
Language(s) (NLP): Bitnet
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: ejbejaranos/Bitnet-Llama3-from8BM-now2B

📄 Description

This repository includes scripts to:

🎯 Transform a Llama3 model to a BitNet architecture.
💻 Train the model using Hugging Face and Weights & Biases.
🚀 Upload the transformed and trained model to Hugging Face for inference and future use.

⚙️ Requirements

Python 3.8+
Pytorch 1.10+
Transformers 4.0+
Hugging Face Hub API
Weights & Biases

🧰 Installation

Make sure you have all required dependencies installed:

pip install torch transformers datasets wandb huggingface_hub

💥 How to Use

Using the trained model for inference

from transformers import AutoModelForCausalLM, AutoTokenizer
from utils.bitnet_transformation import replace_linears_in_hf

# Load the BitNet model
model = "ejbejaranos/Bitnet-Llama3-from8BM-now2B"
model = AutoModelForCausalLM.from_pretrained(
    model,
    use_auth_token="YOUR_HF_TOKEN"
)

# Replace BitNet layers for inference
replace_linears_in_hf(model)
tokenizer = AutoTokenizer.from_pretrained("ejbejaranos/Bitnet-Llama3-from8BM-now2B")

# Set up for inference
model.to(device="cuda:0")
prompt = "What is Machine Learning?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generate_ids = model.generate(inputs.input_ids, max_length=50)
output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

print(output)