Instructions to use Rumiii/Mistral-BioMed-Tool-Caller-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rumiii/Mistral-BioMed-Tool-Caller-7B with Transformers:

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

pipe = pipeline("text-generation", model="Rumiii/Mistral-BioMed-Tool-Caller-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rumiii/Mistral-BioMed-Tool-Caller-7B")
model = AutoModelForCausalLM.from_pretrained("Rumiii/Mistral-BioMed-Tool-Caller-7B", device_map="auto")
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 Settings

vLLM

How to use Rumiii/Mistral-BioMed-Tool-Caller-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rumiii/Mistral-BioMed-Tool-Caller-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rumiii/Mistral-BioMed-Tool-Caller-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rumiii/Mistral-BioMed-Tool-Caller-7B

SGLang

How to use Rumiii/Mistral-BioMed-Tool-Caller-7B 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 "Rumiii/Mistral-BioMed-Tool-Caller-7B" \
    --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": "Rumiii/Mistral-BioMed-Tool-Caller-7B",
		"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 "Rumiii/Mistral-BioMed-Tool-Caller-7B" \
        --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": "Rumiii/Mistral-BioMed-Tool-Caller-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Rumiii/Mistral-BioMed-Tool-Caller-7B with Docker Model Runner:
```
docker model run hf.co/Rumiii/Mistral-BioMed-Tool-Caller-7B
```

Mistral-BioMed-Tool-Caller-7B

A fine-tuned version of Mistral-7B-Instruct-v0.3 specialized for biomedical tool-calling. Given a biomedical question and a set of available tool schemas, the model selects the correct tool and generates properly formatted function-call arguments in Mistral's native [TOOL_CALLS] format.

Model Details

Property	Value
Base model	mistralai/Mistral-7B-Instruct-v0.3
Fine-tuning method	QLoRA (4-bit) via Unsloth
LoRA rank / alpha	32 / 32
Target modules	q/k/v/o_proj, gate/up/down_proj
Trainable parameters	83,886,080 (1.14% of total)
Training dataset	gxx27/BioTool (5,408 examples after filtering) + 540 general-instruction examples (~10% mix)
Epochs	1
Final training loss	~0.27
Hardware	Single Kaggle Tesla T4

Coverage

Trained on tool-calling patterns across 127 tools spanning three biomedical API families:

NCBI E-utilities (esearch, efetch, elink, BLAST, and related endpoints)
UniProt REST (protein, proteome, and taxonomy lookups)
Ensembl REST (coordinate mapping, variant effect prediction, comparative genomics)

This covers genomics, proteomics, and comparative biology tool-use. It does not cover clinical-facing APIs such as ICD-10 lookup, drug databases, or clinical trial registries.

A small slice (~10%) of general-instruction data (HuggingFaceH4/no_robots) was interleaved during training specifically to preserve the base model's general conversational ability alongside its new biomedical specialization.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Rumiii/Mistral-BioMed-Tool-Caller-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

tools = [
    {
        "type": "function",
        "function": {
            "name": "esearch",
            "description": "Search an NCBI Entrez database and return UIDs matching a text query.",
            "parameters": {
                "type": "object",
                "properties": {
                    "db": {"type": "string", "description": "Entrez database name"},
                    "term": {"type": "string", "description": "Search query"},
                },
                "required": ["db", "term"],
            },
        },
    }
]

messages = [{"role": "user", "content": "Search PubMed for articles on BRCA1 mutations."}]

inputs = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Training Data

BioTool: a biomedical function-calling dataset of 7,040 human-verified query-to-API-call pairs across NCBI, UniProt, and Ensembl.

@misc{gao2026biotoolcomprehensivetoolcallingdataset,
      title={BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models},
      author={Xin Gao and Ruiyi Zhang and Meixi Du and Peijia Qin and Pengtao Xie},
      year={2026},
      eprint={2605.05758},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.05758},
}

This model was trained with Unsloth and Hugging Face's TRL library.

License

Apache 2.0

Downloads last month: 470

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Rumiii/Mistral-BioMed-Tool-Caller-7B

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

(528)

this model

Dataset used to train Rumiii/Mistral-BioMed-Tool-Caller-7B

Paper for Rumiii/Mistral-BioMed-Tool-Caller-7B

BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models

Paper • 2605.05758 • Published May 7 • 5