Instructions to use hassanshka/Biomni-R0-32B-PTQ-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hassanshka/Biomni-R0-32B-PTQ-INT8 with Transformers:

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

pipe = pipeline("text-generation", model="hassanshka/Biomni-R0-32B-PTQ-INT8")
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-PTQ-INT8")
model = AutoModelForCausalLM.from_pretrained("hassanshka/Biomni-R0-32B-PTQ-INT8")
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-PTQ-INT8 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-PTQ-INT8"
# 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-PTQ-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hassanshka/Biomni-R0-32B-PTQ-INT8

SGLang

How to use hassanshka/Biomni-R0-32B-PTQ-INT8 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-PTQ-INT8" \
    --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-PTQ-INT8",
		"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-PTQ-INT8" \
        --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-PTQ-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hassanshka/Biomni-R0-32B-PTQ-INT8 with Docker Model Runner:
```
docker model run hf.co/hassanshka/Biomni-R0-32B-PTQ-INT8
```

Biomni-R0-32B-PTQ-INT8

This is a Post-Training Quantization (PTQ) INT8 version of Biomni-R0-32B-Preview.

Quantization Details

Parameter	Value
Scheme	INT8 (8-bit weights and activations)
Method	Post-Training Quantization (PTQ)
Backend	Optimum Quanto
Calibration Samples	120
Model Size	~8-10 GB (vs ~60 GB original BF16)
Memory Reduction	~83-87% compared to BF16

Usage

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hassanshka/Biomni-R0-32B-PTQ-INT8",
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hassanshka/Biomni-R0-32B-PTQ-INT8")

# 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]))

Using vLLM (Recommended)

The quantization may be applied at serving time by vLLM for optimal performance:

from vllm import LLM, SamplingParams

llm = LLM(
    model="hassanshka/Biomni-R0-32B-PTQ-INT8",
    quantization="awq",  # or appropriate quantization method
    trust_remote_code=True
)

sampling_params = SamplingParams(temperature=0.7, top_p=0.95)
prompts = ["Your medical question here"]
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)

Quantization Process

This model was quantized using Optimum Quanto with post-training quantization:

from optimum.quanto import quantize, qint8
from transformers import AutoModelForCausalLM

# Load the base model
model = AutoModelForCausalLM.from_pretrained(
    "biomni/Biomni-R0-32B-Preview",
    torch_dtype=torch.float16
)

# Quantize to INT8
quantize(model, weights=qint8, activations=qint8)

# Save the quantized model
model.save_pretrained("./Biomni-R0-32B-PTQ-INT8")

Performance

Memory Reduction: ~83-87% compared to BF16 (from ~60 GB to ~8-10 GB)
Inference Speed: Faster inference on consumer GPUs
Accuracy: Minimal degradation with proper calibration
Hardware: Compatible with most modern GPUs (no special hardware required)

Model Information

Architecture: Qwen3ForCausalLM
Hidden Size: 5120
Number of Layers: 64
Attention Heads: 64
Key-Value Heads: 8
Vocabulary Size: 151936
Max Position Embeddings: 40960

Quantization Info

This model was quantized from the original Biomni-R0-32B-Preview model using:

Method: PTQ-INT8 (Post-Training Quantization)
Calibration Samples: 120
Backend: Optimum Quanto
Quantization Scheme: INT8 weights and activations

License

Apache 2.0 (same as base model)

Citation

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

@misc{{biomni-r0-32b-preview,
  title={{Biomni-R0-32B-Preview}},
  author={{Biomni Team}},
  year={{2024}},
  url={{https://huggingface.co/biomni/Biomni-R0-32B-Preview}}
}}

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Safetensors

Model size

33B params

Tensor type

F16

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Qwen/Qwen3-32B

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