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
| { | |
| "architectures": [ | |
| "GPTForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "config.GPTConfig", | |
| "AutoModelForCausalLM": "model.GPTForCausalLM" | |
| }, | |
| "block_size": 1024, | |
| "dtype": "float32", | |
| "head_dim": 128, | |
| "hidden_size": 768, | |
| "intermediate_size": 1920, | |
| "labels_are_shifted": true, | |
| "max_position_embeddings": 1024, | |
| "model_type": "gpt", | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 35, | |
| "num_key_value_heads": 2, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 100000.0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "vocab_size": 16384, | |
| "xsa_projection": true | |
| } | |