Text Generation
Transformers
Safetensors
English
gpt
causal-lm
decoder-only
grouped-query-attention
rope
swiglu
boundlessbpe
curriculum-learning
xsa
custom_code
Instructions to use UniversalComputingResearch/Limen0.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UniversalComputingResearch/Limen0.2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UniversalComputingResearch/Limen0.2B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("UniversalComputingResearch/Limen0.2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UniversalComputingResearch/Limen0.2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UniversalComputingResearch/Limen0.2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UniversalComputingResearch/Limen0.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UniversalComputingResearch/Limen0.2B
- SGLang
How to use UniversalComputingResearch/Limen0.2B 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 "UniversalComputingResearch/Limen0.2B" \ --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": "UniversalComputingResearch/Limen0.2B", "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 "UniversalComputingResearch/Limen0.2B" \ --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": "UniversalComputingResearch/Limen0.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UniversalComputingResearch/Limen0.2B with Docker Model Runner:
docker model run hf.co/UniversalComputingResearch/Limen0.2B
- Xet hash:
- 952494d2b18c64349cebfcc30bc850ead0ad93ad7f00c2443b167c45b535ff59
- Size of remote file:
- 705 kB
- SHA256:
- 8135d93e13806e4f78289e9771389b27ae2b83472dc9d9b2a7397a383dfe9cab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.