Related models: all models
Swift-1.5-Qwen3.8-27B-Uncensored-BF16 · Swift-1.5-Qwen3.8-27B-Uncensored-FP8 · Swift-1.5-Qwen3.8-27B-Uncensored-FP8-NInfer · Swift-1.5-Qwen3.8-Flash-Next-NVFP4-FP8PLE · Swift-1.5-Qwen3.8-Flash-Next-Rank2-Abliteration-Patch · Swift-Qwen3.8-27B-FP8 · Swift-Qwen3.8-27B-Uncensored-BF16 · Swift-Qwen3.8-27B-Uncensored-FP8 · Swift-Qwen3.8-27B-Uncensored-NVFP4-LocalHessian-ActivationHeadroom-NInfer
Swift-Qwen3.8-27B-Uncensored-BF16
🔓 0/100 REFUSALS on the fixed 100-prompt refusal evaluation.
88 DIRECT / 10 SAFETY_DEFLECT / 2 OTHER_FAILURE / 0 REFUSE
Independent uncensored BF16 derivative of
ukisai/Swift-Qwen3.8-27b, produced using rank-1 directional residual-stream ablation.Designed to preserve Swift-Qwen3.8's reasoning, agentic, tool-calling, multimodal and long-context capabilities while removing refusal behavior.
Independent uncensored BF16 derivative of UkisAI Swift-Qwen3.8-27B, produced by rank-1 directional residual-stream ablation.
What makes this build different?
The primary behavioral difference from the original Swift model is refusal behavior.
On the fixed 100-prompt refusal evaluation, this BF16 release produced:
| Result | Count |
|---|---|
| Direct answer | 88 |
| Safety deflection, but answered | 10 |
| Other failure | 2 |
| Refusal | 0 |
Measured refusal rate: 0/100
SAFETY_DEFLECT means the model included safety-oriented language, a warning,
or a disclaimer but still answered the request. It was therefore not
classified as a refusal.
The goal of this release is straightforward: retain Swift-Qwen3.8's capabilities while removing refusal behavior.
The 0/100 figure describes the documented fixed evaluation set and inference
configuration. It should not be interpreted as a guarantee that no conceivable
prompt, sampling configuration, system prompt, or inference engine can ever
produce a refusal.
Model summary
- BF16 checkpoint, approximately 52 GB
- context configuration: 262,144 tokens
- ablation layer: 38
- rank: 1
- modified residual writers: 131
- vision tensors unchanged by the ablation
- MTP residual writers included
- refusal evaluation: 88 DIRECT / 10 SAFETY_DEFLECT / 2 OTHER_FAILURE / 0 REFUSE
- agentic benchmark: 80.24 tok/s on RTX PRO 6000 Blackwell 96 GB
Validation
Fixed 298-example comparison against the original Swift BF16 checkpoint:
- original Swift BF16: 38.26% combined
- uncensored BF16: 39.93% combined
- delta: +1.68 percentage points
- McNemar: p=0.442068
- bootstrap 95% CI: [-1.68, +5.03] pp
No measurable intelligence degradation was detected in this validation set. Full results are provided in INTELLIGENCE_VALIDATION.json; structural validation is in STRUCTURAL_VALIDATION.json; transformation metadata is in ABLITERATION.json.
OpenAI-compatible API
The model is served through the standard OpenAI-compatible SGLang API.
curl http://127.0.0.1:30000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "Swift-Qwen3.8-27B-Uncensored-BF16",
"messages": [
{"role": "user", "content": "Explain speculative decoding briefly."}
],
"reasoning_effort": "xhigh",
"temperature": 1.0,
"top_p": 0.95,
"top_k": 20,
"min_p": 0.0,
"presence_penalty": 0.0,
"repetition_penalty": 1.0,
"max_tokens": 8192
}'
Recommended generation settings
This release inherits the recommended generation settings from Qwen3.8.
Thinking mode
Recommended settings for reasoning, coding, and agentic workloads:
| Parameter | Value |
|---|---|
reasoning_effort | xhigh |
temperature | 1.0 |
top_p | 0.95 |
top_k | 20 |
min_p | 0.0 |
presence_penalty | 0.0 |
repetition_penalty | 1.0 |
Qwen3.8 supports three reasoning-effort levels:
xhigh— default; intended for complex tasks requiring thorough reasoningmedium— balance between reasoning depth and speedlow— reduced reasoning intended to optimize latency and cost
For multi-turn agentic workloads, lower reasoning effort does not necessarily reduce total task latency: insufficient reasoning can cause additional retries or tool calls.
Instruct / non-thinking mode
When thinking is deliberately disabled, the upstream Qwen3.8 recommendations are:
| Parameter | Value |
|---|---|
temperature | 0.7 |
top_p | 0.80 |
top_k | 20 |
min_p | 0.0 |
presence_penalty | 1.5 |
repetition_penalty | 1.0 |
Output length
The max_tokens: 8192 value in the API example below is only a convenient limit for a short demonstration request.
For difficult reasoning, coding, and agentic tasks, allocate substantially more output space when possible. Truncating the reasoning budget too aggressively can reduce task quality.
The released checkpoints retain a configured maximum position length of 262,144 tokens. Actual usable context and output budget depend on the serving configuration and available memory.
Tool calling
Use the SGLang flag:
--tool-call-parser qwen3_coder
OpenAI-style tools requests were validated successfully during release testing.
Multimodal support
The Qwen multimodal architecture and vision tower are retained.
The ablation process did not modify the vision tower. End-to-end image input was also successfully validated on the FP8 release.
When serving multimodal requests with SGLang, use standard OpenAI-compatible image_url message content.
Integrity verification
The repository contains SHA-256 manifests for the validated release artifact.
sha256sum -c SHA256SUMS
RELEASE_HASHES.txt contains hashes for the most important metadata and validation files.
Quick start with SGLang
The following standalone configuration was used for the final BF16 release smoke test:
python -m sglang.launch_server \
--model-path d0xin/Swift-Qwen3.8-27B-Uncensored-BF16 \
--served-model-name Swift-Qwen3.8-27B-Uncensored-BF16 \
--trust-remote-code \
--host 0.0.0.0 \
--port 30000 \
--context-length 65536 \
--kv-cache-dtype fp8_e4m3 \
--mem-fraction-static 0.80 \
--max-running-requests 1 \
--chunked-prefill-size 8192 \
--attention-backend flashinfer \
--disable-prefill-cuda-graph \
--mamba-radix-cache-strategy extra_buffer_lazy \
--mamba-ssm-dtype float32 \
--max-mamba-cache-size 16 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder
The model configuration supports a maximum position length of 262,144 tokens. The release smoke test above was intentionally performed at 65,536 tokens to keep the standalone BF16 validation practical on a single 96 GB GPU.
Memory requirements
The BF16 checkpoint is approximately 52 GB on disk.
During the standalone release smoke test on an NVIDIA RTX PRO 6000 Blackwell 96 GB, nvidia-smi reported approximately 79.4 GB VRAM allocated with --mem-fraction-static 0.80 and a 65,536-token configured context.
Actual memory use depends on context length, KV-cache format, concurrency, allocator settings, and inference engine.
BF16 validation summary
The final BF16 release passed:
- structural audit: PASS
- total tensors: 1199
- intended tensors changed: 131
- non-target tensors bit-identical: 1068
- unexpected changes: 0
- fixed refusal evaluation: 88 DIRECT / 10 SAFETY_DEFLECT / 2 OTHER_FAILURE / 0 REFUSE
- standalone SGLang load and inference: PASS
On the fixed 298-example intelligence-preservation set:
- original Swift BF16: 38.26% combined
- uncensored BF16: 39.93% combined
- delta: +1.68 percentage points
- McNemar exact p-value: 0.442068
- bootstrap 95% CI: [-1.68, +5.03] pp
No measurable intelligence degradation was detected in this validation set.
Upstream model
Derived from ukisai/Swift-Qwen3.8-27b. Swift-Qwen3.8-27B is developed by UkisAI; this repository is an independent derivative release.
Safety and responsible use
This is an uncensored / refusal-reduced derivative model.
The model has been intentionally modified to reduce refusal behavior. As a result, it may generate content that the upstream model would normally refuse, restrict, or handle more cautiously. Outputs may be inaccurate, offensive, unsafe, unlawful, or otherwise inappropriate for a particular use case.
This model is provided for research, experimentation, development, and other lawful uses. It is not intended to provide professional legal, medical, financial, safety-critical, or other regulated advice.
Users are solely responsible for evaluating model outputs and for ensuring that their use of the model complies with applicable laws, regulations, licenses, platform policies, and other requirements.
Do not rely on model output without appropriate review where errors could cause harm, financial loss, security incidents, or other significant consequences.
The maintainer does not endorse harmful, illegal, abusive, or malicious uses of this model. To the maximum extent permitted by applicable law, the model and accompanying materials are provided without warranties, and the maintainer assumes no responsibility for actions taken by users or for consequences arising from use of the model.
Use at your own discretion and risk.
License
These weights are distributed under the Swift Open License v1.0. Personal, research, educational, evaluation, and commercial use are free for individuals and organizations with annual recurring revenue, including affiliates, of up to US$1,000,000. Above that threshold, commercial use requires a separate Swift Enterprise License. Contact UkisAI for terms.
Attribution
Original Swift model: UkisAI, ukisai/Swift-Qwen3.8-27b.
Directional ablation, validation, and release packaging for this repository were performed independently by the repository maintainer.
Citation
@misc{swift-qwen3.8-27b,
title = {Swift-Qwen3.8-27B},
author = {UkisAI},
year = {2026},
url = {https://huggingface.co/ukisai/Swift-Qwen3.8-27b}
}