# Swift Qwen3.8 27B GGUF

URL: https://interfaze.ai/models/ukisaiswift-qwen38-27b-gguf

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Swift Qwen3.8 27B GGUF by ukisai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Swift Qwen3.8 27B GGUF | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Swift Qwen3.8 27B GGUF | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF)

Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, using **58.3% fewer thinking tokens** while maintaining near-identical performance (**<1% loss**) and as a result getting a **x1.95 speed-up** on several tasks.

## Evaluation scope

> All results below compare the Qwen3.8-27B BF16 base with the same base plus the Swift adapter.

## Benchmarks

## Quantized evaluations

Quantized deployment is the intended use for Swift: lower-memory weights paired with shorter reasoning. The INT4 evaluations below come from the [source model card](https://huggingface.co/ukisai/Swift-Qwen3.8-27b) and were run on W4A16 and AWQ checkpoints, not on this F16 GGUF. They retain token savings across GPQA, IFBench, and AIME. On AIME, Swift matches or improves accuracy and reduces output-cap failures by **31–33%**.

Each row compares the same quantized base with and without the Swift adapter. GPQA and AIME use five seeds; IFBench uses four samples per prompt and strict scoring. Output caps: GPQA 100,000; IFBench 81,920; AIME 32,768. GPQA and IFBench use saved historical base runs. AIME uses template-default effort and counts truncated answers as incorrect. Its shorter cap makes it a separate comparison from the BF16 table.

## GGUF quantizations

Mean KL divergence against the BF16 source, lower is better. Tiers marked _new tier_ were added on 2026-09-13 and carry the wikitext @512 measurement and 512-token top-token agreement; their 32k columns will be filled as those runs complete. `wikitext` is wikitext-2 test; `held-out` is our own chat and long-document set, reserved before the importance matrix was fitted. `Top-p` is top-token agreement with BF16 at 32k on the held-out set.

**Read the two 32k columns together.** On this hybrid architecture (48 of 64 blocks are recurrent), a small fraction of positions (about 0.1%) diverge sharply at long context for every tier, including `Q8_0`, and the same is true of the public `Q4_K_M` and `Q8_0` builds of the base Qwen3.8-27B measured on the same harness. Those rare positions dominate the held-out mean; the median divergence at 32k is within 10% of the 512-token value for every tier. Typical-token quality does not degrade with context. The pick below follows the 99th-percentile tail on the held-out set: 2.60 for `Q4_K_M`, 1.75 for `Q5_K_M`, 0.46 for `Q6_K`, 0.23 for `Q8_0`.

All tiers use the same importance matrix (8,016 chunks of domain, prompt and long-document text) and pin the recurrent gate projections `ssm_alpha` and `ssm_beta` to F32 and the MTP head to Q8\_0. `Q4_K_M` additionally lifts `ssm_out`, `attn_gate`, `output` and `token_embd` to Q6\_K; `Q5_K_M` and `Q6_K` lift `attn_gate` to Q8\_0. The lifts cost about 1.1 GB on `Q4_K_M` and reduce its KL divergence by roughly 20% against a plain llama.cpp `Q4_K_M` of the same model.

The tiers added on 2026-09-13 (`IQ2_XXS` through `Q6_K_L`) use the same importance matrix and the same `ssm_alpha`/`ssm_beta` F32 and MTP Q8\_0 pins, with per-tensor type layouts computed for this model by [bartowski's quantization-config](https://github.com/bartowski1182/quantization-config) instead of llama.cpp's built-in heuristic (`--tensor-type-file`). `Q4_K_L`, `Q6_K_S` and `Q6_K_L` are the large and small layouts of `Q4_K_M` and `Q6_K`. All files were built with llama.cpp release b10896 from a BF16 conversion of the published safetensors and checked against BF16 on the harness above.

### KV cache

Only 16 of the 64 blocks are full attention, so the cache stays small for a 27B:

`16 layers x 4 kv-heads x 256 head_dim x 2 (K+V) x 2 bytes = 64 KiB per token`

## Training approach

We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in Qwen’s reasoning rollouts. We then fine-tuned Qwen by penalizing usage of those tokens while it reasons.

Swift produces shorter reasoning traces. In our testing, we also observe fewer overthinking errors.

For maximum gains, Swift also includes a transfer component derived from [BottleCap AI's ThinkingCap-Qwen3.6-27B](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B).

## How to use

### llama.cpp

These files run with [llama.cpp](https://github.com/ggml-org/llama.cpp), installable in one line via [llama.app](https://llama.app/). The command below is the llama.cpp counterpart of the vLLM configuration on the source card: full 262,144 context, thinking on at reasoning effort `xhigh`, reasoning and tool calls parsed from the embedded chat template, and Qwen3.8's thinking-mode sampling.

`curl -LsSf https://llama.app/install.sh | sh llama-server -hf ukisai/Swift-Qwen3.8-27B-GGUF:Q4_K_M \ --jinja -fa on -ngl 99 \ -c 262144 \ --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0 \ --presence-penalty 0 --repeat-penalty 1.0 \ --port 8000`

llama-server exposes an OpenAI-compatible API and a built-in chat web UI on the port above. Swap `Q4_K_M` for any tier in the table above (`IQ2_XXS` up to `Q8_0`) or `F16`; `-hf` fetches the tier and the vision projector automatically. The KV cache costs 64 KiB per token (16 GB at the full 262,144 context), so lower `-c` if it does not fit. Use a recent llama.cpp release with Qwen3.5/Qwen3.8 architecture support. The same sampling values are stored in the GGUF header and `xhigh` is the template default; the flags above make the configuration explicit.

They also work in [LM Studio](https://lmstudio.ai/), [koboldcpp](https://github.com/LostRuins/koboldcpp) and [Jan AI](https://www.jan.ai/). In those apps set the same sampling values by hand and a context length of at least 65,536 tokens; the default 4,096-token window overflows on long reasoning and looks like an endless loop.

#### Multimodal

This model supports image input. Alongside the quants, this repo includes the multimodal projector file [mmproj-Swift-Qwen3.8-27B-F16.gguf](https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF/blob/main/mmproj-Swift-Qwen3.8-27B-F16.gguf), which pairs with any tier above. llama.cpp downloads the mmproj automatically when using `-hf` as shown above; if you are loading files manually, pass it with `--mmproj`.

#### MTP

This model has MTP (Multi-Token Prediction) layers, and they are included in every tier, stored at Q8\_0. MTP layers act as a built-in draft model, letting llama.cpp run speculative decoding for faster generation. To use them, add the following flag to your llama.cpp command:

`--spec-type draft-mtp --spec-draft-n-max 3`

This is the counterpart of the vLLM `--speculative-config '{"method":"mtp","num_speculative_tokens":3}'` option.

### Ollama

`ollama create swift -f <(curl -fsSL https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF/resolve/main/Modelfile) && ollama run swift`

Use Ollama 0.33 or newer. The [`Modelfile`](https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF/blob/main/Modelfile) in this repo pulls the `Q4_K_M` tier together with the vision projector, applies the sampling values above, and sets Ollama's built-in Qwen3.8 renderer and parser, which separate reasoning from the answer and parse tool calls. Swift's embedded chat template is identical to Qwen3.8's. For another tier, download the Modelfile, change the tag after `FROM`, and run `ollama create swift -f Modelfile`.

`ollama run hf.co/ukisai/Swift-Qwen3.8-27B-GGUF:Q4_K_M` also works without a Modelfile. It runs the embedded chat template through llama.cpp and reads the sampling values from the [`params`](https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF/blob/main/params) file. Hugging Face cannot set Ollama's renderer and parser, so in this mode reasoning may appear inline with the answer.

Ollama sizes the context window from VRAM: 262,144 tokens with 48 GB or more, 32,768 with 24 GB, and 4,096 below that, which is too short for long reasoning. To raise it, start the server with `OLLAMA_CONTEXT_LENGTH=65536 ollama serve` or run `/set parameter num_ctx 65536` in the chat. To use the MTP layers, run `/set parameter draft_num_predict 3`.

### UkisAI API

If you would rather not run the weights yourself, Swift is served through an OpenAI-compatible API at `https://ukisai.com/api/swift/v1`. It is **free for research purposes** and needs no API key. The model id is `swift`.

```
curl https://ukisai.com/api/swift/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "swift", "messages": [{"role": "user", "content": "Hello, Swift."}]}'
```

## Validation

The converted files passed a finite-tensor check and a CPU text-generation smoke test. Multimodal generation and the full benchmark suite have **not** been re-evaluated on this GGUF release. The results above and on the [source model card](https://huggingface.co/ukisai/Swift-Qwen3.8-27b) come from the BF16 and INT4 checkpoints named there, not from these files.

## License and access

Swift weights are distributed through gated access 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](https://ukisai.com/contact) for terms.

## Citation

```
@misc{swift-qwen3.8-27b,
  title  = {Swift-Qwen3.8-27B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-Qwen3.8-27b}
}
```

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