Instructions to use Technoculture/BioMistral-Hermes-Dare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Technoculture/BioMistral-Hermes-Dare with Transformers:

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

pipe = pipeline("text-generation", model="Technoculture/BioMistral-Hermes-Dare")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Technoculture/BioMistral-Hermes-Dare")
model = AutoModelForCausalLM.from_pretrained("Technoculture/BioMistral-Hermes-Dare")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Technoculture/BioMistral-Hermes-Dare with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Technoculture/BioMistral-Hermes-Dare

SGLang

How to use Technoculture/BioMistral-Hermes-Dare 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 "Technoculture/BioMistral-Hermes-Dare" \
    --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": "Technoculture/BioMistral-Hermes-Dare",
		"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 "Technoculture/BioMistral-Hermes-Dare" \
        --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": "Technoculture/BioMistral-Hermes-Dare",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Technoculture/BioMistral-Hermes-Dare with Docker Model Runner:
```
docker model run hf.co/Technoculture/BioMistral-Hermes-Dare
```

BioMistral-Hermes-Dare

BioMistral-Hermes-Dare is a merge of the following models:

Evaluations

Benchmark	BioMistral-Hermes-Dare	Orca-2-7b	llama-2-7b	meditron-7b	meditron-70b
MedMCQA
ClosedPubMedQA
PubMedQA
MedQA
MedQA4
MedicationQA
MMLU Medical
MMLU
TruthfulQA
GSM8K
ARC
HellaSwag
Winogrande

More details on the Open LLM Leaderboard evaluation results can be found here.

🧩 Configuration

models:
  - model: BioMistral/BioMistral-7B-DARE
    parameters:
      weight: 1.0
  - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
    parameters:
      weight: 0.6
merge_method: linear
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Technoculture/BioMistral-Hermes-Dare"
messages = [{"role": "user", "content": "I am feeling sleepy these days"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 64

Safetensors

Model size

7B params

Tensor type

F16

Collection including Technoculture/BioMistral-Hermes-Dare

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