Instructions to use mychen76/openmixtral-6x7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mychen76/openmixtral-6x7b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="mychen76/openmixtral-6x7b-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mychen76/openmixtral-6x7b-v2")
model = AutoModelForCausalLM.from_pretrained("mychen76/openmixtral-6x7b-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mychen76/openmixtral-6x7b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mychen76/openmixtral-6x7b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mychen76/openmixtral-6x7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mychen76/openmixtral-6x7b-v2

SGLang

How to use mychen76/openmixtral-6x7b-v2 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 "mychen76/openmixtral-6x7b-v2" \
    --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": "mychen76/openmixtral-6x7b-v2",
		"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 "mychen76/openmixtral-6x7b-v2" \
        --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": "mychen76/openmixtral-6x7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mychen76/openmixtral-6x7b-v2 with Docker Model Runner:
```
docker model run hf.co/mychen76/openmixtral-6x7b-v2
```

openmixtral-6x7b-v2 / README.md

mychen76

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metadata

license: apache-2.0
tags:
  - merge

openmixtral-6x7b-v2

Quantized openmixtral-6x7b-merged_v2 is a merge of the following 6x7B models:

🧩 Configuration

base_model: mlabonne/Marcoro14-7B-slerp
experts:
  - source_model: openchat/openchat-3.5-1210
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
  - source_model: Weyaxi/Einstein-v4-7B
    positive_prompts:
    - "physics"
    - "biology"
    - "chemistry"
    - "science"    
  - source_model: BioMistral/BioMistral-7B
    positive_prompts:
    - "medical"
    - "pubmed"
    - "healthcare"
    - "health"        
  - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: maywell/PiVoT-0.1-Starling-LM-RP
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
  - source_model: WizardLM/WizardMath-7B-V1.1
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"
tokenizer_source: union

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mychen76/openmixtral-6x7b-v2"

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": "Why the sky is blue"}]
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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	72.33
AI2 Reasoning Challenge (25-Shot)	68.52
HellaSwag (10-Shot)	86.75
MMLU (5-Shot)	65.11
TruthfulQA (0-shot)	65.13
Winogrande (5-shot)	79.87
GSM8k (5-shot)	68.61