Instructions to use NousResearch/OLMo-Bitnet-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NousResearch/OLMo-Bitnet-1B with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/OLMo-Bitnet-1B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/OLMo-Bitnet-1B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NousResearch/OLMo-Bitnet-1B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NousResearch/OLMo-Bitnet-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NousResearch/OLMo-Bitnet-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/OLMo-Bitnet-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NousResearch/OLMo-Bitnet-1B

SGLang

How to use NousResearch/OLMo-Bitnet-1B 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 "NousResearch/OLMo-Bitnet-1B" \
    --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": "NousResearch/OLMo-Bitnet-1B",
		"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 "NousResearch/OLMo-Bitnet-1B" \
        --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": "NousResearch/OLMo-Bitnet-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NousResearch/OLMo-Bitnet-1B with Docker Model Runner:
```
docker model run hf.co/NousResearch/OLMo-Bitnet-1B
```

OLMo-Bitnet-1B / README.md

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metadata

license: apache-2.0
datasets:
  - allenai/dolma

OLMo-Bitnet-1B

OLMo-Bitnet-1B is a 1B parameter model trained using the method described in The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

It was trained on the first 60B tokens of the Dolma dataset, so it is merely a research proof-of-concept to test out the methodolgy.

A separate training run was run with the exact same hyperparameters, but using standard fp16 weights. The comparison can be found in this wandb report.

Sample inference code

pip install ai2-olmo

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("NousResearch/OLMo-Bitnet-1B")
model = AutoModelForCausalLM.from_pretrained("NousResearch/OLMo-Bitnet-1B",
    torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")

streamer = TextStreamer(tokenizer)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, pad_token_id=tokenizer.eos_token_id,
    temperature=0.8, repetition_penalty=1.1, do_sample=True,streamer=streamer)
pipe("The capitol of Paris is",  max_new_tokens=256)

Training was performed using OLMo.