Instructions to use hassanshka/Biomni-R0-32B-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hassanshka/Biomni-R0-32B-AWQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="hassanshka/Biomni-R0-32B-AWQ-INT4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hassanshka/Biomni-R0-32B-AWQ-INT4")
model = AutoModelForCausalLM.from_pretrained("hassanshka/Biomni-R0-32B-AWQ-INT4")
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 hassanshka/Biomni-R0-32B-AWQ-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hassanshka/Biomni-R0-32B-AWQ-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hassanshka/Biomni-R0-32B-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hassanshka/Biomni-R0-32B-AWQ-INT4

SGLang

How to use hassanshka/Biomni-R0-32B-AWQ-INT4 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 "hassanshka/Biomni-R0-32B-AWQ-INT4" \
    --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": "hassanshka/Biomni-R0-32B-AWQ-INT4",
		"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 "hassanshka/Biomni-R0-32B-AWQ-INT4" \
        --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": "hassanshka/Biomni-R0-32B-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hassanshka/Biomni-R0-32B-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/hassanshka/Biomni-R0-32B-AWQ-INT4
```

Biomni-R0-32B-AWQ-INT4

This is an AWQ INT4 quantized version of Biomni-R0-32B-Preview.

Quantization Details

Parameter	Value
Scheme	W4A16 (4-bit weights, 16-bit activations)
Method	AWQ (Activation-aware Weight Quantization)
Group Size	128
Calibration	Custom biomedical dataset
Framework	LLM Compressor

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hassanshka/Biomni-R0-32B-AWQ-INT4",
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hassanshka/Biomni-R0-32B-AWQ-INT4")

# Inference
messages = [{"role": "user", "content": "Your medical question here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Quantization Script

from llmcompressor.modifiers.awq import AWQModifier
from llmcompressor import oneshot

recipe = AWQModifier(
    scheme="W4A16",
    targets="Linear",
    ignore=["lm_head"],
)

oneshot(
    model=model,
    dataset=calibration_data,
    recipe=recipe,
    max_seq_length=2048,
    num_calibration_samples=len(calibration_data),
)

Performance

Memory Reduction: ~75% compared to BF16
Inference Speed: Optimized for consumer GPUs (RTX 3090/4090)
Accuracy: Minimal degradation due to custom calibration on biomedical data

License

Apache 2.0 (same as base model)

Citation

If you use this model, please cite the original Biomni model.

Downloads last month: 207

Safetensors

Model size

6B params

Tensor type

F32

I64

I32

Model tree for hassanshka/Biomni-R0-32B-AWQ-INT4

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biomni/Biomni-R0-32B-Preview

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