Instructions to use just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq with Transformers:

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

pipe = pipeline("text-generation", model="just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq")
model = AutoModelForMultimodalLM.from_pretrained("just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq", device_map="auto")
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 Settings

vLLM

How to use just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq

SGLang

How to use just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq 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 "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq" \
    --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": "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq",
		"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 "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq" \
        --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": "just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq with Docker Model Runner:
```
docker model run hf.co/just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq
```

Qwen3.8-27B-Uncensored-MXFP4-awq

MXFP4 quantization of orcarouter/Qwen3.8-27B-Uncensored, made with AMD Quark and exported as an AWQ-style checkpoint for serving with vLLM on AMD RDNA4 GPUs (gfx1201 / RX 9000 series) with ROCm.

What I used to run it

Start with this image: https://hub.docker.com/r/stilldeadcode/vllm-radiance

Add this if needed (needed at the time of testing): https://codeberg.org/ggz14/radiance-vllm-mxfp4

NOTE: fp8_mtp.py was already ran on this model, it is not needed to run again.

Ran Livebench Math for quality check (GLM 5.3 was grader): Final scoreboard (official 182q math, xhigh, 150k tokens) AMPS_Hard 77.0% math_comp 97.7% olympiad 100% Overall 86.5%

Model Details


Source model	orcarouter/Qwen3.8-27B-Uncensored
Architecture	Qwen3.5-27B (Qwen3_5ForConditionalGeneration)
Quantization tool	AMD Quark
Quantization	MXFP4 (Quark export, `quant_method: quark`)
KV cache	Post-RoPE KV quantization enabled
Context length	262,144
Hidden size / layers	5120 / 64
Attention	24 heads, 4 KV heads (GQA)
Vocab size	248,320
Multimodal	Vision tower present (excluded from quantization)

Quantization excludes lm_head and the vision tower, so those run at higher precision.

Usage

Serve with vLLM:

vllm serve just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq \
  --port 8081

Requires a vLLM build with Quark/MXFP4 support (ROCm on RDNA4 recommended).

Intended Use

Uncensored/abliterated fine-tune — intended for research, creative writing, and local inference where refusal behavior is not desired.
Single-file model.safetensors (~19 GB), sized to fit consumer GPUs with 24 GB+ VRAM (depending on context length and offloading).

Limitations

MXFP4 is an aggressive quantization; expect some quality loss vs bf16/fp8.
Uncensored models may produce harmful or objectionable output. You are responsible for how you use this model.
Not tested for production or safety-critical use.

License

Apache-2.0, inherited from the base model.

Downloads last month: 920

Safetensors

Model size

16B params

Tensor type

BF16

F8_E4M3

Model tree for just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq

Base model

Qwen/Qwen3.8-27B

Finetuned

orcarouter/Qwen3.8-27B-Uncensored

Quantized

(57)

this model