Instructions to use Fu01978/Llama-3.2-1B-4B-Quad-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fu01978/Llama-3.2-1B-4B-Quad-MoE with Transformers:

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

pipe = pipeline("text-generation", model="Fu01978/Llama-3.2-1B-4B-Quad-MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/Llama-3.2-1B-4B-Quad-MoE")
model = AutoModelForCausalLM.from_pretrained("Fu01978/Llama-3.2-1B-4B-Quad-MoE")
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 Fu01978/Llama-3.2-1B-4B-Quad-MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/Llama-3.2-1B-4B-Quad-MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/Llama-3.2-1B-4B-Quad-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fu01978/Llama-3.2-1B-4B-Quad-MoE

SGLang

How to use Fu01978/Llama-3.2-1B-4B-Quad-MoE 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 "Fu01978/Llama-3.2-1B-4B-Quad-MoE" \
    --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": "Fu01978/Llama-3.2-1B-4B-Quad-MoE",
		"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 "Fu01978/Llama-3.2-1B-4B-Quad-MoE" \
        --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": "Fu01978/Llama-3.2-1B-4B-Quad-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fu01978/Llama-3.2-1B-4B-Quad-MoE with Docker Model Runner:
```
docker model run hf.co/Fu01978/Llama-3.2-1B-4B-Quad-MoE
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.2-1B-4B-Quad-MoE

This is a Mixture of Experts (MoE) model based on unsloth/Llama-3.2-1B-Instruct. It merges a base model, an instruct model, a coding specialist, and a math specialist into a single sparse architecture.

Model Details

Total Parameters: 3.65B
Active Parameters per Token: 1.24B
Base Model: unsloth/Llama-3.2-1B-Instruct
Experts:
1. unsloth/Llama-3.2-1B-Instruct (General Purpose)
2. unsloth/Llama-3.2-1B (Creative/Prose)
3. cutelemonlili/Llama3.2-1B-Instruct_Lean_Code (Coding Specialist)
4. prithivMLmods/Llama-Express.1-Math (Logic/Mathematics)

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Fu01978/Llama-3.2-1B-4B-Quad-MoE")
tokenizer = AutoTokenizer.from_pretrained("Fu01978/Llama-3.2-1B-4B-Quad-MoE")

Downloads last month: 4

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Fu01978/Llama-3.2-1B-4B-Quad-MoE

cutelemonlili/Llama3.2-1B-Instruct_Lean_Code

prithivMLmods/Llama-Express.1-Math

unsloth/Llama-3.2-1B

unsloth/Llama-3.2-1B-Instruct

Merge model

this model

Quantizations

3 models