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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	---
	license: apache-2.0
	tags:
	- merge
	---

	# openmixtral-6x7b-v2

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

	## 🧩 Configuration

	```yaml
	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
	```python
	!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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mychen76__openmixtral-6x7b-v2)


	\| 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\|