Instructions to use LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData with Transformers:

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

pipe = pipeline("text-generation", model="LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData

SGLang

How to use LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData 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/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData" \
    --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": "LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData",
		"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 "LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData" \
        --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": "LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/Qwen3.6-27B-Terminal-SFT-2Epoch-HF-FSDP-2BData
```

gyung commited on Jun 3

Commit

643adb5

verified ·

1 Parent(s): 0209b4e

Update model card with pending TB2-lite evaluation status

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README.md +2 -2

README.md CHANGED Viewed

@@ -21,8 +21,8 @@ base_model: Qwen/Qwen3.6-27B
 - Base model: `Qwen/Qwen3.6-27B`
 - Training setup: `2 epochs, HF FSDP full fine-tuning, 2BData setting`
-- Model card snapshot: `2026-05-09 00:58:04 UTC`
-- Corrected TB2-lite evaluated results currently indexed: `56`
 - Corrected TB2-lite score: `pending / not matched in current result directory`
 ## Quickstart

 - Base model: `Qwen/Qwen3.6-27B`
 - Training setup: `2 epochs, HF FSDP full fine-tuning, 2BData setting`
+- Model card snapshot: `2026-06-03 22:09:22 UTC`
+- Corrected TB2-lite evaluated results currently indexed: `60`
 - Corrected TB2-lite score: `pending / not matched in current result directory`
 ## Quickstart