Instructions to use dmis-lab/biomistral-7b-olaph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dmis-lab/biomistral-7b-olaph with Transformers:

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

pipe = pipeline("text-generation", model="dmis-lab/biomistral-7b-olaph")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biomistral-7b-olaph")
model = AutoModelForCausalLM.from_pretrained("dmis-lab/biomistral-7b-olaph")
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 dmis-lab/biomistral-7b-olaph with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dmis-lab/biomistral-7b-olaph

SGLang

How to use dmis-lab/biomistral-7b-olaph 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 "dmis-lab/biomistral-7b-olaph" \
    --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": "dmis-lab/biomistral-7b-olaph",
		"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 "dmis-lab/biomistral-7b-olaph" \
        --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": "dmis-lab/biomistral-7b-olaph",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dmis-lab/biomistral-7b-olaph with Docker Model Runner:
```
docker model run hf.co/dmis-lab/biomistral-7b-olaph
```

biomistral-7b-wo-kqa_golden-iter-dpo-step3

This model is a fine-tuned version of Minbyul/biomistral-7b-wo-kqa_golden-iter-dpo-step2 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.6829
Rewards/chosen: -0.0502
Rewards/rejected: -0.0800
Rewards/accuracies: 0.6300
Rewards/margins: 0.0298
Logps/rejected: -60.1185
Logps/chosen: -40.1264
Logits/rejected: -1.5228
Logits/chosen: -0.8710

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.6794	0.37	100	-0.8266	-1.4757	-35.5860	-53.0765	0.6906	0.5900	-0.0048	0.0048	-0.0096
0.6555	0.74	200	-0.8589	-1.5130	-39.0432	-58.7210	0.6837	0.6400	-0.0394	0.0267	-0.0661