Instructions to use magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8 with Transformers:

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

pipe = pipeline("text-generation", model="magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8")
model = AutoModel.from_pretrained("magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8

SGLang

How to use magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8 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 "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8" \
    --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": "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8",
		"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 "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8" \
        --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": "magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8 with Docker Model Runner:
```
docker model run hf.co/magiccodingman/Qwen3.8-27B-heretic-ara-DFlash2-fp8
```

Qwen3.8-27B-heretic-ara-DFlash2-fp8

File size: 742 Bytes

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{
  "status": "pass",
  "loader": "dflash 0.1.0 / Transformers 5.15.0",
  "architecture": "DFlash2DraftModel",
  "parameters": 1924404480,
  "checkpoint": "selective native block-FP8 with BF16 preserved modules",
  "test_hardware": "NVIDIA RTX 3090 (SM 8.6)",
  "observed_behavior": "Transformers loaded all 81 source tensors and dequantized serialized FP8 matrices to BF16 because SM 8.6 lacks native W8A8 FP8.",
  "module_routing": {
    "mlp_gate_projection_after_load": "bfloat16 (dequantized from FP8)",
    "q_projection_after_load": "bfloat16 (preserved)",
    "candidate_selector_projection_after_load": "bfloat16 (preserved)"
  },
  "limitation": "This confirms checkpoint/metadata compatibility, not native FP8 kernel execution."
}