Text Generation
Transformers
Safetensors
English
lfm2
linux
bash
shell
command-generation
conversational
Instructions to use thealper2/lfm2-700m-linux-command with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thealper2/lfm2-700m-linux-command with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thealper2/lfm2-700m-linux-command") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thealper2/lfm2-700m-linux-command") model = AutoModelForCausalLM.from_pretrained("thealper2/lfm2-700m-linux-command", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thealper2/lfm2-700m-linux-command with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thealper2/lfm2-700m-linux-command" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thealper2/lfm2-700m-linux-command", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/thealper2/lfm2-700m-linux-command
- SGLang
How to use thealper2/lfm2-700m-linux-command 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 "thealper2/lfm2-700m-linux-command" \ --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": "thealper2/lfm2-700m-linux-command", "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 "thealper2/lfm2-700m-linux-command" \ --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": "thealper2/lfm2-700m-linux-command", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use thealper2/lfm2-700m-linux-command with Docker Model Runner:
docker model run hf.co/thealper2/lfm2-700m-linux-command
Download train_record.json from thealper2/lfm2-700m-linux-command: direct link, hf CLI and curl.
- Browser
- Download file 16.5 kB
-
https://huggingface.co/thealper2/lfm2-700m-linux-command/resolve/main/train_record.json
- Command line
-
hf download hf://thealper2/lfm2-700m-linux-command/train_record.json
-
curl -L -o train_record.json https://huggingface.co/thealper2/lfm2-700m-linux-command/resolve/main/train_record.json
16.5 kB
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| "stage": "train", | |
| "timestamp": "2026-09-21T11:15:01+00:00", | |
| "model_name": "LiquidAI/LFM2-700M", | |
| "seed": 42, | |
| "config": { | |
| "paths": { | |
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| "reports_dir": "reports", | |
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| "data": { | |
| "nl2bash_repo": "jiacheng-ye/nl2bash", | |
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