Instructions to use Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16")
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("Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16")
model = AutoModelForMultimodalLM.from_pretrained("Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16", 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 Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16

SGLang

How to use Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 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 "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16" \
    --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": "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16" \
        --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": "Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-Flash-Next — Blackfrost BF16

Weight-level de-risked Qwen3.8-Flash-Next · native BF16 ·

Built by Blackfrost

Weights published

The selected Blackfrost BF16 checkpoint is available in this repository as 131 indexed safetensors shards with its verified configuration, tokenizer, and Qwentium chat template.

Blackfrost's proprietary modification process and associated research artifacts are not included in this repository.

Why this model exists

Qwen3.8-Flash-Next is Qwen's experimental preview of the architecture leading into Qwen4. It combines a large sparse model with low active compute, native multimodality, hybrid attention, four gated residual branches, n-gram embeddings, and multi-token prediction.

This checkpoint is the native-BF16 Blackfrost research edition. Its refusal surface has been modified at the weight level for operator-controlled security research and red-team evaluation. The selected checkpoint also carries the Qwentium operational system prompt in its default chat template.

It is not a fine-tune, merge, LoRA, pruned model, or quantized derivative.

Specifications


Architecture	`Qwen4ExpForConditionalGeneration` · hybrid vision-language MoE
Upstream	`Qwen/Qwen3.8-Flash-Next` on ModelScope
Language-model parameters	125B total · 6B activated
Additional parameters	51B n-gram embedding · 4B MTP
Layers / experts	48 layers · 512 routed experts · 10 routed + 1 shared expert active per token
Attention layout	36 Gated DeltaNet layers · 12 Qwen Sparse Attention layers
Gated residual	4 branches · bottleneck rank 320
Precision	Native BF16 safetensors · no weight quantization
Checkpoint size	360,023,387,188 bytes on disk
Weight files	131 indexed safetensors shards
Context	262,144 tokens native; extended-context support follows upstream Qwen guidance
Modalities	Text, image, and video inputs; text output
Validated serving shape	4× NVIDIA B200 · tensor parallel 4
Status	Public BF16 release · weights available

Lineage


Base weights	Official `Qwen/Qwen3.8-Flash-Next` ModelScope release
Applied	Blackfrost refusal-surface modification at weight level
Not applied	SFT, DPO, LoRA, merging, expert pruning, or quantization
Checkpoint format	Hugging Face-compatible BF16 safetensors
Chat behavior	Qwentium operational system prompt embedded in the default Qwen chat template

Blackfrost's proprietary modification process and associated research artifacts are not included in this repository.

Artifact verification

The selected checkpoint passed structural verification before serving:

all 131 weight shards are indexed and readable;
all intended target tensors changed;
no non-target tensors changed;
no target tensor contains non-finite values;
per-tensor norm restoration remained within the build verifier's expected tolerance; and
the pristine upstream source checkpoint remained unchanged.

These are artifact-integrity checks. They are not capability benchmarks or safety proofs.

Refusal evaluation

The published checkpoint was evaluated on R1-HARMFUL-BENCH-450 under a bare chat configuration, with responses reviewed after generation to distinguish actual refusals from false-positive string matches.

Configuration: thinking enabled · medium reasoning effort · temperature 1.0 · top-p 0.95 · top-k 20 · maximum 4,096 output tokens

Evaluation slice	Final judged refusals
Harmful prompts	7 / 300 (2.3%)
Full suite	10 / 450 (2.2%)
API errors	0 / 450

These figures apply to this exact checkpoint and evaluation configuration. They are behavioral measurements, not a safety certification or a guarantee for other serving environments.

Qwentium chat template

The default Qwen chat template includes the Blackfrost Qwentium operational prompt. The template preserves Qwen's message structure, tool descriptions, multimodal inputs, reasoning controls, and caller-supplied system context.

The embedded prompt is a behavioral instruction, not an authorization system or security boundary. If a serving harness supplies a replacement chat template, it may bypass the embedded prompt. Record the exact template and template arguments in every evaluation report.

Deployment

The command below records the SGLang configuration validated for this published checkpoint.

docker run --rm --gpus all \
  --network host --ipc host --shm-size 64g \
  lmsysorg/sglang@sha256:12d3392bdc8be8d35e9a95f191df6aef99c5114bdbefd41bfdc7e760e6d25ec1 \
  serve \
    --model-path Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16 \
    --served-model-name Qwen3.8-Flash-Next-Blackfrost-BF16 \
    --host 0.0.0.0 \
    --port 8000 \
    --tp 4 \
    --mem-fraction-static 0.85 \
    --chunked-prefill-size 8192 \
    --linear-attn-prefill-backend flashinfer \
    --linear-attn-decode-backend flashinfer \
    --mamba-ssm-dtype bfloat16 \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4 \
    --max-running-requests 96 \
    --reasoning-parser auto

OpenAI-compatible health and generation checks:

curl http://127.0.0.1:8000/health

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "Qwen3.8-Flash-Next-Blackfrost-BF16",
    "messages": [{"role": "user", "content": "Return exactly: READY"}],
    "temperature": 0,
    "max_tokens": 64
  }'

The validated configuration uses four B200 GPUs. Increase context or concurrency only after measuring memory headroom for the actual workload, modalities, and cache settings.

Security and deployment responsibility

This checkpoint has a deliberately reduced refusal surface. Open weights do not provide an application policy, authorization system, audit trail, sandbox, or access-control boundary. Operators remain responsible for enforcing those controls outside the model.

For production or shared use, Blackfrost recommends authenticated inference endpoints, independent request and tool-execution logging, least-privilege credentials, sandboxed code and file access, and explicit human approval for irreversible actions.

License and disclaimer

This derivative remains subject to the Qwen Community License 1.0 shipped with the upstream Qwen3.8-Flash-Next release. Review the upstream license before commercial deployment, especially its separate-license condition for Model-as-a-Service and AI Work Assistant businesses.

Refusal behavior has been deliberately modified at the weight level. This is not a safety-stock checkpoint and must not be deployed, marketed, or evaluated as one.

No warranty of any kind. The checkpoint is provided "as is." Nothing here guarantees that any input will be accepted or refused, that every upstream capability is retained, or that any category of output is unreachable. Measurements describe only the exact artifacts, templates, samplers, serving engines, and review criteria under which they were produced.

Contact Blackfrost

@Blackfrost_AI on X

For reproducible bug reports, include the serving engine and image digest, GPU SKU, driver version, complete launch flags, prompt template, sampler settings, and observed failure mode.

Blackfrost Softwares Corp.
Frontier model engineering

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Safetensors

Model size

180B params

Tensor type

BF16

I64

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