Instructions to use shadowml/Mixolar-4x7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shadowml/Mixolar-4x7b with Transformers:

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

pipe = pipeline("text-generation", model="shadowml/Mixolar-4x7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shadowml/Mixolar-4x7b")
model = AutoModelForCausalLM.from_pretrained("shadowml/Mixolar-4x7b")
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 shadowml/Mixolar-4x7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shadowml/Mixolar-4x7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shadowml/Mixolar-4x7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shadowml/Mixolar-4x7b

SGLang

How to use shadowml/Mixolar-4x7b 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 "shadowml/Mixolar-4x7b" \
    --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": "shadowml/Mixolar-4x7b",
		"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 "shadowml/Mixolar-4x7b" \
        --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": "shadowml/Mixolar-4x7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shadowml/Mixolar-4x7b with Docker Model Runner:
```
docker model run hf.co/shadowml/Mixolar-4x7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mixolar-4x7b

This model is a Mixure of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:

🧩 Configuration

base_model: kyujinpy/Sakura-SOLAR-Instruct
gate_mode: hidden
experts:
  - source_model: kyujinpy/Sakura-SOLAR-Instruct
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: jeonsworld/CarbonVillain-en-10.7B-v1
    positive_prompts:
    - "write"
    - "AI"
    - "text"
    - "paragraph"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: rishiraj/meow
    positive_prompts:
    - "chat"
    - "say"
    - "what"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"
    negative_prompts:
    - "chat"
    - "assistant"
    - "storywriting"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Mixolar-4x7b"

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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.18
AI2 Reasoning Challenge (25-Shot)	71.08
HellaSwag (10-Shot)	88.44
MMLU (5-Shot)	66.29
TruthfulQA (0-shot)	71.81
Winogrande (5-shot)	83.58
GSM8k (5-shot)	63.91

Downloads last month: 8,420

Safetensors

Model size

36B params

Tensor type

F16

Model tree for shadowml/Mixolar-4x7b

Quantizations

2 models

Space using shadowml/Mixolar-4x7b 1

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

71.080
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.440
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

66.290
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

71.810
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.580
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.910