Instructions to use LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU")
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

vLLM

How to use LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU

SGLang

How to use LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU 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 "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU" \
    --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": "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU",
		"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 "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU" \
        --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": "LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU
```

gyung commited on 16 days ago

Commit

acb41cd

verified ·

1 Parent(s): 11fa144

Update model card with pending TB2-lite evaluation status

Browse files

Files changed (1) hide show

README.md +38 -0

README.md ADDED Viewed

	@@ -0,0 +1,38 @@

+---
+language:
+- en
+- ko
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- terminal
+- sft
+- vllm
+- tb2-lite
+- evaluation-pending
+base_model: LiquidAI/LFM2-8B
+---
+# LLM-OS-Models/LFM2-8B-Terminal-SFT-2Epoch-Unsloth-7GPU
+터미널 작업 자동화를 위한 Terminal SFT 모델입니다. 입력된 작업/이전 터미널 상태를 보고 다음에 실행할 명령을 JSON 형태로 생성하는 용도로 학습했습니다.
+## 모델 요약
+- Base model: `LiquidAI/LFM2-8B`
+- Training setup: `2 epochs, Unsloth SFT`
+- Model card snapshot: `2026-05-08 16:04:13 UTC`
+- Corrected TB2-lite evaluated results currently indexed: `56`
+- Corrected TB2-lite score: `pending / not matched in current result directory`
+## 평가 상태
+- Current corrected TB2-lite score: `pending`
+- Reason: 현재 `/home/work/.data/tb2_lite_eval/corrected_readme_models_vllm` 집계 결과와 이 HF repo명이 직접 매칭되지 않았습니다.
+- Next step: 동일한 `tb2_lite/scripts/replay_eval.py` 경로로 평가를 돌린 뒤 점수 카드로 자동 교체합니다.
+## 모델군 해석
+- LFM 계열은 빠른 sec/step과 큰 SFT 반응성이 장점입니다. 이 repo는 아직 현재 집계 JSON과 직접 매칭되는 점수가 없어 별도 평가가 필요합니다.
+- TB2-lite 점수는 일반 지능 벤치마크가 아니라 터미널 next-action JSON 재현 능력을 측정합니다.
+- 생성 명령은 실제 실행 전에 sandbox, allowlist, human review 같은 안전장치를 거쳐야 합니다.