Text Generation
Transformers
Safetensors
English
fuse3
mixture-of-experts
MoE
coding
python
code-generation
LFM2
Qwen
LiquidAI
small-language-model
SLM
agentic
fusion
expert-routing
5B
efficient-inference
conversational
Eval Results (legacy)
Instructions to use Akahsizrr/fuse-1-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akahsizrr/fuse-1-Lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Akahsizrr/fuse-1-Lite") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Akahsizrr/fuse-1-Lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Akahsizrr/fuse-1-Lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Akahsizrr/fuse-1-Lite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akahsizrr/fuse-1-Lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Akahsizrr/fuse-1-Lite
- SGLang
How to use Akahsizrr/fuse-1-Lite 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 "Akahsizrr/fuse-1-Lite" \ --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": "Akahsizrr/fuse-1-Lite", "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 "Akahsizrr/fuse-1-Lite" \ --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": "Akahsizrr/fuse-1-Lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Akahsizrr/fuse-1-Lite with Docker Model Runner:
docker model run hf.co/Akahsizrr/fuse-1-Lite
- Xet hash:
- da291354e9c6b02ef79f24462c8edbd343e14dabf045b9edbe512220f5fd90db
- Size of remote file:
- 17.9 MB
- SHA256:
- 0b124e06b0d81002864892c3ebb866effe115aa1d5fbe8e6e9e23254655354a8
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