Instructions to use HachiML/mistral_2x7b_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HachiML/mistral_2x7b_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="HachiML/mistral_2x7b_v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HachiML/mistral_2x7b_v0.1")
model = AutoModelForCausalLM.from_pretrained("HachiML/mistral_2x7b_v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HachiML/mistral_2x7b_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HachiML/mistral_2x7b_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/mistral_2x7b_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HachiML/mistral_2x7b_v0.1

SGLang

How to use HachiML/mistral_2x7b_v0.1 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 "HachiML/mistral_2x7b_v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/mistral_2x7b_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "HachiML/mistral_2x7b_v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/mistral_2x7b_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HachiML/mistral_2x7b_v0.1 with Docker Model Runner:
```
docker model run hf.co/HachiML/mistral_2x7b_v0.1
```

mistral_2x7b_v0.1

mistral_2x7b_v0.1 is a Mixure of Experts (MoE) made with the following models using mergekit-moe:

🧩 Configuration

gate_mode: hidden # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
experts:
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts:
      - "What are some fun activities to do in Seattle?"
      - "What are the potential long-term economic impacts of raising the minimum wage?"
  - source_model: nvidia/OpenMath-Mistral-7B-v0.1-hf
    positive_prompts:
     - "What is 27 * 49? Show your step-by-step work."
     - "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "HachiML/mistral_2x7b_v0.1"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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: 11

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for HachiML/mistral_2x7b_v0.1

mistralai/Mistral-7B-Instruct-v0.2

nvidia/OpenMath-Mistral-7B-v0.1-hf

Merge model

this model