Instructions to use LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU with Transformers:

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

pipe = pipeline("text-generation", model="LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU")
model = AutoModelForImageTextToText.from_pretrained("LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU"
# 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/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU

SGLang

How to use LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU 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/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU" \
    --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/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU",
		"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/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU" \
        --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/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU
```

gyung commited on 16 days ago

Commit

48b2bfb

verified ·

1 Parent(s): 61b3229

Update model card with pending TB2-lite evaluation status

Browse files

Files changed (1) hide show

README.md +24 -23

README.md CHANGED Viewed

@@ -1,37 +1,38 @@
 ---
-license: apache-2.0
 library_name: transformers
-base_model: google/gemma-4-E2B-it
 tags:
-- terminal-agent
-- qwen3.5
-- full-finetuning
-- unsloth
 - tb2-lite
 ---
 # LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU
-## Summary
-- Base model: `google/gemma-4-E2B-it`
-- Source output root: `/home/work/.data/qwen_sft/models/google__gemma-4-E2B-it__terminal_sft_2epoch_ddp_4gpu`
-- Root export copied from: `checkpoint-734`
-- Included checkpoints: `checkpoint-367, checkpoint-734`
-## Layout
-- Repository root contains the eval/inference-ready final export.
-- Trainer checkpoints are included as subdirectories for recovery and inspection.
-## Notes
-Gemma 4 E2B text-only terminal SFT, 4-GPU DDP, epoch checkpoints included.
-## Loading
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU", trust_remote_code=True)
-```

 ---
+language:
+- en
+- ko
 library_name: transformers
+pipeline_tag: text-generation
 tags:
+- terminal
+- sft
+- vllm
 - tb2-lite
+- evaluation-pending
+base_model: google/gemma-4-E2B-it
 ---
 # LLM-OS-Models/gemma-4-E2B-it-Terminal-SFT-2Epoch-DDP-4GPU
+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: `google/gemma-4-E2B-it`
+- Training setup: `2 epochs, DDP fine-tuning`
+- Model card snapshot: `2026-05-08 16:04:24 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
+- Gemma 계열은 native Gemma/Liquid 전처리와 chat template 처리가 중요합니다. 이 repo는 corrected 평가가 끝나면 점수 카드로 교체합니다.
+- 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.