CoolFace
Modelpublic

Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16

sourceHugging Faceotherupdated 1mo agoView on Hugging Face
4likes269downloads
Model Card

<div align="center">

Blackfrost

<h1>Qwen3.8-Flash-Next — Blackfrost BF16</h1>

<h3>Weight-level de-risked Qwen3.8-Flash-Next · native BF16 ·</h3>

<p><strong>Built by <a href="https://x.com/Blackfrost_AI">Blackfrost</a></strong></p>

<p> <img src="https://img.shields.io/badge/Format-BF16-2563eb?style=for-the-badge" /> <img src="https://img.shields.io/badge/Architecture-125BMoE-111827?style=for-the-badge" /> <img src="https://img.shields.io/badge/Active-6B-047857?style=for-the-badge" /> <img src="https://img.shields.io/badge/Status-WEIGHTSLIVE-047857?style=for-the-badge" /> </p>

</div>

### 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

ArchitectureQwen4ExpForConditionalGeneration · hybrid vision-language MoE
Upstream`Qwen/Qwen3.8-Flash-Next` on ModelScope
Language-model parameters125B total · 6B activated
Additional parameters51B n-gram embedding · 4B MTP
Layers / experts48 layers · 512 routed experts · 10 routed + 1 shared expert active per token
Attention layout36 Gated DeltaNet layers · 12 Qwen Sparse Attention layers
Gated residual4 branches · bottleneck rank 320
PrecisionNative BF16 safetensors · no weight quantization
Checkpoint size360,023,387,188 bytes on disk
Weight files131 indexed safetensors shards
Context262,144 tokens native; extended-context support follows upstream Qwen guidance
ModalitiesText, image, and video inputs; text output
Validated serving shape4× NVIDIA B200 · tensor parallel 4
StatusPublic BF16 release · weights available

Lineage

Base weightsOfficial Qwen/Qwen3.8-Flash-Next ModelScope release
AppliedBlackfrost refusal-surface modification at weight level
Not appliedSFT, DPO, LoRA, merging, expert pruning, or quantization
Checkpoint formatHugging Face-compatible BF16 safetensors
Chat behaviorQwentium 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 sliceFinal judged refusals
Harmful prompts7 / 300 (2.3%)
Full suite10 / 450 (2.2%)
API errors0 / 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.

bash
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:

bash
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

<div align="center">

<h3><a href="https://x.com/BlackfrostAI">@BlackfrostAI</a> on X</h3>

<p>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.</p>

<p><strong>Blackfrost Softwares Corp.</strong><br />Frontier model engineering</p>

</div>


<div align="center">

<p><strong>Qwen3.8-Flash-Next — Blackfrost BF16</strong> · © 2026 Blackfrost Softwares Corp.<br /> <a href="https://x.com/BlackfrostAI">@BlackfrostAI</a></p>

</div>