Text Generation
Transformers
Safetensors
lfm2
liquid
lfm2.5
edge
parallel-constrained-decoding
structured-generation
classification
inference-only
modal
conversational
Instructions to use monotykamary/LFM2.5-2.6B-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monotykamary/LFM2.5-2.6B-RLCD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="monotykamary/LFM2.5-2.6B-RLCD") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("monotykamary/LFM2.5-2.6B-RLCD") model = AutoModelForCausalLM.from_pretrained("monotykamary/LFM2.5-2.6B-RLCD", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use monotykamary/LFM2.5-2.6B-RLCD with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "monotykamary/LFM2.5-2.6B-RLCD" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "monotykamary/LFM2.5-2.6B-RLCD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/monotykamary/LFM2.5-2.6B-RLCD
- SGLang
How to use monotykamary/LFM2.5-2.6B-RLCD 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 "monotykamary/LFM2.5-2.6B-RLCD" \ --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": "monotykamary/LFM2.5-2.6B-RLCD", "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 "monotykamary/LFM2.5-2.6B-RLCD" \ --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": "monotykamary/LFM2.5-2.6B-RLCD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use monotykamary/LFM2.5-2.6B-RLCD with Docker Model Runner:
docker model run hf.co/monotykamary/LFM2.5-2.6B-RLCD
Download requirements-pcd.txt from monotykamary/LFM2.5-2.6B-RLCD: direct link, hf CLI and curl.
- Browser
- Download file 187 Bytes
-
https://huggingface.co/monotykamary/LFM2.5-2.6B-RLCD/resolve/main/requirements-pcd.txt
- Command line
-
hf download hf://monotykamary/LFM2.5-2.6B-RLCD/requirements-pcd.txt
-
curl -L -o requirements-pcd.txt https://huggingface.co/monotykamary/LFM2.5-2.6B-RLCD/resolve/main/requirements-pcd.txt
187 Bytes
| torch==2.14.0 | |
| transformers==5.17.0 | |
| accelerate==1.15.0 | |
| huggingface-hub==1.31.0 | |
| safetensors==0.8.0 | |
| jsonschema==4.26.0 | |
| jinja2==3.1.6 | |
| numpy==2.4.6 | |
| fastapi==0.141.1 | |
| modal==1.5.5 | |
| pytest==9.1.1 | |