Instructions to use allenai/BAR-2x7B-Code-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/BAR-2x7B-Code-SFT with Transformers:

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

pipe = pipeline("text-generation", model="allenai/BAR-2x7B-Code-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/BAR-2x7B-Code-SFT")
model = AutoModelForCausalLM.from_pretrained("allenai/BAR-2x7B-Code-SFT")
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

vLLM

How to use allenai/BAR-2x7B-Code-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/BAR-2x7B-Code-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/BAR-2x7B-Code-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/BAR-2x7B-Code-SFT

SGLang

How to use allenai/BAR-2x7B-Code-SFT 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 "allenai/BAR-2x7B-Code-SFT" \
    --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": "allenai/BAR-2x7B-Code-SFT",
		"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 "allenai/BAR-2x7B-Code-SFT" \
        --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": "allenai/BAR-2x7B-Code-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/BAR-2x7B-Code-SFT with Docker Model Runner:
```
docker model run hf.co/allenai/BAR-2x7B-Code-SFT
```

BAR

BAR (Branch-Adapt-Route) is a modular post-training approach that extends a fully post-trained language model with new domain capabilities via independently trained Mixture-of-Experts. Rather than retraining a single model across all domains, BAR trains independent domain experts — each through its own mid-training, supervised finetuning (SFT), and reinforcement learning pipeline — and composes them into a unified model via an MoE architecture with lightweight router training.

All BAR models are built on top of Olmo 2 7B.

Models in the BAR suite

BAR-7B — initial fully post-trained 7B dense model (the starting point)
BAR-2x7B-Base — 2-expert MoE (anchor + base pre-trained model)
BAR-2x7B-Math-SFT — math expert after mid-training and SFT
BAR-2x7B-Math — math expert after mid-training + SFT + RLVR
BAR-2x7B-Code-SFT — code expert after mid-training and SFT
BAR-2x7B-Code — code expert after mid-training + SFT + RLVR
BAR-2x7B-Tool-Use — tool use expert (SFT only)
BAR-2x7B-Safety — safety expert (SFT only)
BAR-5x7B — final 5-expert MoE combining all experts with a trained router

Results

Model	Overall	Knowledge	Reasoning	Chat	Math	Code	Tool Use	Safety
BAR-7B	31.3	28.5	29.8	48.9	23.6	11.8	25.3	51.3
BAR-2x7B-Math-SFT	36.8	28.8	31.2	40.9	41.9	20.5	21.6	72.7
BAR-2x7B-Math	39.3	29.0	30.8	42.5	55.8	22.1	19.8	75.4
BAR-2x7B-Code-SFT	38.5	28.8	29.1	40.1	25.5	49.3	19.7	77.3
BAR-2x7B-Code	38.8	28.5	29.2	41.0	26.9	50.4	19.8	75.3
BAR-2x7B-Tool-Use	37.2	28.5	28.7	39.3	21.8	16.9	46.4	79.1
BAR-2x7B-Safety	35.6	28.7	28.8	38.1	22.4	15.7	21.1	94.6
BAR-5x7B	49.1	28.4	30.8	38.7	56.2	49.9	45.6	94.0

Scores are unweighted averages across benchmarks within each category. See the paper for per-benchmark results and full evaluation details.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

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Safetensors

Model size

12B params

Tensor type

BF16

Collection including allenai/BAR-2x7B-Code-SFT

Branch-Adapt-Route

Collection

Artifacts for Branch-Adapt-Route • 9 items • Updated 17 days ago • 6