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 / fp8_profile_ab_comparison.json

magiccodingman

Upload folder using huggingface_hub

b6bbe8a verified about 1 month ago

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	{
	"decision": "select mlp-only profile",
	"reason": "Preserving attention output projections costs approximately 105 MB but improves proposal KL, top-1 agreement, and hidden-state fidelity.",
	"profiles": {
	"mlp_plus_attention_output_fp8": {
	"tensor_payload_bytes": 2407368960,
	"quantized_matrices": 20,
	"proposal_kl_mean": 0.0026533013419426316,
	"proposal_top1_agreement": 0.9285714030265808,
	"top16_candidate_overlap": 0.9754464285714286,
	"draft_hidden_relative_rmse": 0.03659051767294803,
	"draft_hidden_cosine_similarity": 0.9993306398391724,
	"selector_path_token_agreement": 0.8571429252624512
	},
	"mlp_only_fp8": {
	"tensor_payload_bytes": 2512200960,
	"quantized_matrices": 15,
	"proposal_kl_mean": 0.0018929995239503794,
	"proposal_top1_agreement": 0.9642857313156128,
	"top16_candidate_overlap": 0.9765625,
	"draft_hidden_relative_rmse": 0.03063499420217177,
	"draft_hidden_cosine_similarity": 0.9995341897010803,
	"selector_path_token_agreement": 0.8571429252624512
	}
	}
	}