Instructions to use aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP with Transformers:

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

pipe = pipeline("text-generation", model="aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP")
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("aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP")
model = AutoModelForMultimodalLM.from_pretrained("aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP", 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 aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP

SGLang

How to use aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP 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 "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP" \
    --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": "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP",
		"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 "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP" \
        --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": "aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP with Docker Model Runner:
```
docker model run hf.co/aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP
```

Qwen3.8 Flash-Next Uncensored NVFP4 + MTP

This is a load-tested sibling of mazinb/Qwen3.8-Flash-Next-Uncensored-NVFP4 with the model's one-layer MTP draft experts converted to NVFP4. It is a roughly 125B-total / 6B-active multimodal mixture-of-experts checkpoint intended for high-throughput speculative decoding on NVIDIA Blackwell.

What is quantized

All 48 target-model routed MoE expert banks: NVFP4 (inherited from the source checkpoint)
MTP routed experts (512) and the MTP shared expert: NVFP4
Target-model shared experts: BF16
PLE n-gram tables: BF16, qualified with pinned CPU residency and host-to-device lookup
Attention, linear-attention/GDN, vision, embeddings, gates, and other excluded modules retain the source checkpoint's declared precision

The conversion is deterministic ModelOpt-compatible RTN with group size 16 and no calibration dataset. MTP_NVFP4_MANIFEST.json records source identity, conversion details, preservation closure, and tool version. SHA256SUMS covers every distributed model file; its SHA-256 is 17e1223cb7670c85e52ad56f243bf2702a5325f4ed2e94693a23b4ffedba0292.

Qualified runtime

The exact checkpoint passed end-to-end vLLM qualification on one NVIDIA RTX PRO 6000 Blackwell 96 GB GPU with:

88.9 GiB hard VRAM budget, preserving at least 6 GiB physical VRAM
131,072-token serving context
MTP speculative depth 3
BF16 KV cache with PLE-only CPU offload
161.12 output tokens/s in the gated single-stream decode-after-prefill test
974 accepted draft tokens out of 1,689 observed during qualification
120,031-token long-context, multimodal, tool-call, reasoning-channel, readiness, behavior, and resource checks

This architecture requires a recent compatible vLLM build with Qwen4-Exp/Flash-Next, Blackwell NVFP4 MoE, MTP, and PLE CPU-offload support. Stock older vLLM releases may not load it. Use trust_remote_code only if your runtime policy permits it, and verify SHA256SUMS before serving.

Lineage

Uncensored NVFP4 parent: mazinb/Qwen3.8-Flash-Next-Uncensored-NVFP4 at revision f2c21eb3d2ff5f24c208ea7e3afba65e2e70f83f
The parent model card documents its behavioral merge/source lineage.
This repository changes MTP expert storage and the one source shard that formerly contained superseded BF16 MTP expert tensors; all other checkpoint tensors are byte-preserved.

Safety and use

This model is intentionally abliterated/uncensored and may comply with unsafe, illegal, inaccurate, or harmful requests. It has no warranty. Users are responsible for evaluation, access controls, applicable law, and downstream use.

Donations

If this release is useful, Bitcoin donations are welcome:

bc1q5ayht3fxhj0v95fk0z8l2f6900g3awdsw5842p

Downloads last month: -

Safetensors

Model size

118B params

Tensor type

BF16

I64

Model tree for aday777/Qwen3.8-Flash-Next-Uncensored-NVFP4-MTP

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

mazinb/Qwen3.8-Flash-Next-Uncensored-NVFP4

Quantized

(1)

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