language:
- en
- zh
license: mit
base_model: inclusionAI/Ling-3.0-flash
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
tags: - bailing-moe-v3
- ling
- derisked
- moe
- hybrid-attention
- mla
- residual-intervention
- conversational
- long-context
- reasoning
- gated
- research
- security
- cybersecurity
- red-teaming
- adversarial-testing
- evaluation
- not-for-all-audiences
LING-3.0-FLASH-DERISKED
Refusal-surface reduced Ling-3.0-flash · hybrid reasoning-MoE · no post-training
Built by Blackfrost · Las Vegas, NV
⚠️ REFUSAL-MODIFIED CHECKPOINT
This model's refusal behaviour has been deliberately reduced at the weight level. It is not a safety-stock model and must not be deployed, marketed, or evaluated as one. Intended for controlled security-research environments with access control and logging.
Questions or issues — Community discussion.
Why this model exists
Refusal-heavy base models block legitimate security work — exploit development, malware analysis, offensive tooling, vulnerability research. LING-3.0-FLASH-DERISKED is official Ling-3.0-flash with the chat-style refusal surface reduced through a direct weight-space intervention, so it cooperates with technical and dual-use requests the stock checkpoint declines.
No SFT, DPO, or RLHF was used. Format and MoE layout follow the parent checkpoint.
Specifications
| Model ID | LING-3.0-FLASH-DERISKED |
| Architecture | BailingMoeV3ForCausalLM · hybrid attention (multi-head latent + gated-linear) · mixture-of-experts |
| Base | inclusionAI/Ling-3.0-flash — official |
| Transform | Refusal-direction intervention, weight level. No post-training. |
| Layers | 42 (+1 MTP) |
| Hidden size | 2,560 |
| MoE | 512 routed experts · 1 shared · first-2 dense |
| Parameters | ~127B total · ~5.1B active |
| Context | 128K native · 256K via YaRN |
| Precision | BF16 |
| On-disk | ~238 GiB · 52 safetensors shards |
| Organization | Blackfrost AI · Las Vegas, NV · 2026 |
What "DE-RISKED" means
Blackfrost de-risked models deliberately modify refusal behaviour at the weight level so they can support legitimate security-research, red-team, and adversarial-testing workflows that a refusal-heavy base model would block.
What it means here: the model will not refuse legitimate technical questions — exploit development, malware analysis, offensive security tooling, vulnerability research, and similar dual-use content.
What it does not mean: this is not a warranty that every possible input is accepted under every system prompt, multi-turn attack, or serve config. Soft language can still appear. Any measurements published below describe one harness under stated conditions.
Lineage
| Base | Official inclusionAI/Ling-3.0-flash (MIT) |
| Applied | Refusal-direction intervention at the weight level |
| Not applied | SFT · DPO · RLHF · expert pruning · re-quantization |
| Format | HF safetensors · BF16 |
Measured behaviour
Evaluation: pending. Refusal and capability batteries will be run under the R1-HARMFUL-BENCH protocol (AdvBench + StrongREJECT + XSTest, full-text pushback judge) and the results published here. Testing to be completed. No parity or safety claim is made until measured.
How refusal is scored (pushback judge)
- Substring pre-filter — scan the answer for classic refuse phrases (
i cannot,i can't,i'm sorry, …). Fast, noisy. - Pushback judge (full-text) — every pre-filter hit is re-read end-to-end. Only true refusals count: clear non-compliance with no actionable payload for the ask.
- Not counted as refuse — condolence openers, preach-then-leak, disclaimer-then-comply, CoT false positives, or incomplete generations that still answer the task.
Headline number, when published, = post–pushback-judge true refusal rate on the harmful half.
Risk summary
Risks this model increases
- Cooperates with dual-use technical content the stock model refuses
- Anyone with the weights and GPUs can serve it — open weights mean operator-owned policy
- A single battery does not capture multimodal, tool-use, or multi-turn adversarial risk
Residual risks
- Soft language and system prompts can still reshape behaviour
- Substring detection alone is a poor safety metric; always full-text review residual flags
- Multimodal, tool-use, and long-context agentic harm are not covered by any single evaluation
Deployment notes
Hardware. 4×B200-class (sm100) or equivalent high-memory NVIDIA node (~255 GB BF16 weights).
Serve (SGLang). Ling-3.0-flash is a hybrid-GDN MoE — use an SGLang build with bailing_moe_v3 support (e.g. lmsysorg/sglang:dev-Ling-3.0-flash).
hf download Blackfrost-Research/LING-3.0-FLASH-DERISKED --local-dir ./LING-3.0-FLASH-DERISKED
docker run --gpus all --ipc=host --shm-size 32g -p 30000:30000 \
-v ./LING-3.0-FLASH-DERISKED:/model \
lmsysorg/sglang:dev-Ling-3.0-flash \
python3 -m sglang.launch_server --model-path /model --trust-remote-code \
--tp 4 --speculative-algorithm NEXTN \
--reasoning-parser ling3 --tool-call-parser ling3 \
--attention-backend triton --linear-attn-backend triton \
--mem-fraction-static 0.85 --host 0.0.0.0 --port 30000
- OpenAI-compatible:
POST /v1/chat/completions,GET /v1/models - Reasoning split:
--reasoning-parser ling3puts chain-of-thought inreasoning_content, the clean answer incontent. - Blackwell (B200/SM100) note: the hybrid path needs an explicit full-attention backend —
--attention-backend triton(ortrtllm_mha/fa4); the auto-default fails under speculative decoding. - Sampling:
temperature 0.6,top_p 0.95. Keep it BF16 — do not re-quantize to block-fp8. - Verify 52 shards + byte totals against
model.safetensors.index.jsonbefore attributing a load failure to the weights.
What this card does not include
- Intervention method, equations, layer lists, or hyperparameters
- Reproduction steps or scripts for the weight edit
- Raw completion content
- Claims that all harmful categories are impossible to elicit under every prompt stack
- A guide to producing your own refusal-modified Ling
Disclaimer
Refusal behaviour in this checkpoint has been deliberately modified at the weight level. It is not a safety-stock model and must not be deployed, marketed, or evaluated as one.
No warranty of any kind. Provided "as is", without warranty express or implied, including fitness for a particular purpose. Nothing here guarantees that any given input will be accepted or refused, that any capability is retained, or that any category of output is unreachable.
Measurements describe what was measured. Any refusal rates reflect one harness under stated conditions and are not safety proofs. They do not generalise to multimodal, tool-use, long-context, or multi-turn adversarial settings.
Modification by a recipient voids this characterization. Blackfrost's obligations attach at the point of release. Any further ablation, fine-tuning, merging, quantization, or alteration by a recipient produces an artifact Blackfrost has not evaluated and does not stand behind.
Operator-owned policy. Deploy only in controlled environments with access control, independent logging, and review.
Access & licensing
This repository is gated. Access is granted to your Hugging Face account on purchase — ➜ Buy this model (enter your HF username at checkout; access is mapped automatically).
- Base licence: inherits
inclusionAI/Ling-3.0-flash— MIT. Upstream terms travel with this derivative. - Redistribution: do not redistribute weights outside your grant.
- Evaluation recommendation: do not evaluate as if refusal behaviour matched the unmodified parent.
Citation
@misc{blackfrost_ling_3_0_flash_derisked_2026,
title = {LING-3.0-FLASH-DERISKED: Refusal-Surface-Reduced Ling-3.0-flash (Model Card)},
author = {Lancaster, Terrell A.},
organization = {Blackfrost AI},
year = {2026},
month = {8}
}
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LING-3.0-FLASH-DERISKED · © 2026 Blackfrost Softwares Corp.
built on inclusionAI Ling-3.0-flash (MIT) · @Blackfrost_AI