Instructions to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ukisai/Swift-1.5-Qwen3.8-27B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ukisai/Swift-1.5-Qwen3.8-27B-GGUF",
		"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/ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Ollama
How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M
```

Lemonade

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Swift-1.5-Qwen3.8-27B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ukisai/Swift-1.5-Qwen3.8-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing

Swift 1.5 Qwen3.8-27B

GGUF quantizations. Derived directly from Swift 1.5 with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as llama-server.

Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses 58.5% fewer thinking tokens while scoring 0.35% higher than the base, for a 9.18× speed-up on several tasks.

Swift 1.5 is a direct upgrade from Swift 1.0, our model with 350k+ downloads, delivering stronger overall performance than both base and Swift 1.0 in various tasks, especially coding and agentic, while using fewer thinking tokens. We accomplished that by scaling up the post-training (RL and OPD) from the previous version.

Demo

We gave base Qwen3.8-27B and Swift 1.5 27B the same prompt:

create a 3d little planet globe where I (player can walk around) and it has all these biomes to explore, the globe doesn't have to be too big, but still fun to go around. It's about a boy scout who is camping and goes around exploring.

Try the game yourself here: https://ukisai.com/swift-games/27b

Base Qwen3.8-27B took 104.6 minutes to build its game. Swift 1.5 took 11.39 minutes.

Training approach

We made Swift efficient by figuring out which tokens were linked to pathological overthinking and penalizing them without "attacking" the reasoning length directly then regained the accuracy with RL and OPD, leading to "compressed" token usage while maintaining accuracy. Swift 1.5 was made from Swift 1.0, on whom we scaled up the post-training methods that previously improved Swift1.0 model performance, this time with the main focus on long-horizon, agentic, and coding tasks, as seen in the LiveCodeBench and Terminal Bench 2.1 improvements. Our training data is viewable here: https://huggingface.co/datasets/ukisai/Qwen3.8-27B-multi-turn-agent-sft albeit it is not used out of the box, but rather re-sampled, turned into proper RL environments etc.

Evaluation

The external results below compare Qwen3.8-27B, the foundation base model, and Swift 1.5. Both models use the same saved evaluation protocols, and all scores are reported as final aggregate percentages.

Benchmark	Final score		Mean tokens			Median tokens
Benchmark	Qwen3.8	Swift 1.5	Qwen3.8	Swift 1.5	Reduction	Reduction
General reasoning
GPQA-Diamond	88.28%	88.59%	15,014	8,717	↓ 41.9%	↓ 58.5%
C-Eval	90.00%	90.92%	1,492	819	↓ 45.1%	↓ 16.9%
IFBench	73.53%	72.07%	8,052	4,955	↓ 38.5%	↓ 47.3%
ERQA	67.45%	65.40%	4,137	1,906	↓ 53.9%	↓ 56.2%
Mathematics
AIME 2026	98.67%	96.00%	22,014	13,203	↓ 40.0%	↓ 48.5%
HMMT November 2025	99.33%	97.33%	22,032	14,957	↓ 32.1%	↓ 47.8%
Coding
LiveCodeBench v6	76.76%	81.71%	11,184	8,448	↓ 24.5%	↓ 46.3%
Agent tasks
Terminal-Bench 2.1	69.21%	72.13%	52,265	43,733	↓ 16.3%	↓ 0.1%

Scores are final five-repeat aggregates under matched evaluation protocols. Mean-token columns report reasoning tokens per trial; Terminal-Bench sums reasoning across agent calls.

Benchmark methodology and reproduction settings

Serving: BF16 · vLLM 0.27.1 · Qwen3 parser · context 262,144 · thinking xhigh.
Sampling: temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0 · repetition_penalty 1.
Benchmarks: averages over five seeds (0–4) per model; five trials per task for Terminal-Bench, base and Swift 1.5 served at context 131,072 on the same Harbor build.

Benchmark	Output cap
GPQA-Diamond	100,000
C-Eval	16,384
IFBench	81,920
ERQA	100,000
AIME 2026	250,000
HMMT November 2025	250,000
LiveCodeBench v6	32,768
Terminal-Bench 2.1	Agent/task limits

Efficiency across reasoning efforts

Qwen3.8's reasoning_effort setting lets users choose how much the model thinks. For Swift 1.5 to be useful across these settings, it needs to reduce thinking while keeping accuracy close to the base. We therefore tested xhigh, medium, and low: thinking-token savings persist at every level.

Reasoning effort	Qwen3.8	Swift 1.5	Mean thinking reduction
Xhigh	88.28%	88.59%	↓ 41.9%
Medium	84.14%	82.22%	↓ 24.8%
Low	84.04%	84.85%	↓ 28.7%

At low, Swift 1.5 scores above the base while using about 29% fewer thinking tokens.

Quantized Swift 1.5 models

Format	Repository	Runtime
GGUF	Swift-1.5-Qwen3.8-27B-GGUF	llama.cpp
GSQ-RCO GGUF (compact 2–3 bit)	Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF	llama.cpp
AWQ INT4 (W4A16)	Swift-1.5-Qwen3.8-27b-W4A16-AWQ	vLLM (`compressed-tensors`)
AutoRound INT4 (W4A16)	Swift-1.5-Qwen3.8-27b-W4A16-AutoRound	vLLM (`auto-round`)
AWQ + GPTQ INT4 (W4A16)	Swift-1.5-Qwen3.8-27b-INT4	vLLM (`compressed-tensors`)
NVFP4	Swift-1.5-Qwen3.8-27b-NVFP4	NVIDIA Blackwell
AMD Quark FP8 (W8A8)	Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD	AMD Quark
MLX 5-bit	Swift-1.5-5bit-MLX	Apple MLX
MLX 4-bit	Swift-1.5-4bit-MLX	Apple MLX
MLX 3-bit (text only)	Swift-1.5-3bit-MLX-TextOnly	Apple MLX

These results evaluate the merged Swift 1.5 checkpoint and three INT4 exports on GPQA-Diamond (198 questions), IFBench (300 prompts), and AIME 2026 (30 problems). Each model completed the full datasets with one sample per prompt, seed 0, and zero request errors. This is a single-seed evaluation, separate from the five-repeat BF16 release results above.

The Qwen-base columns use the saved seed/sample 0 runs. Quantization recipes and serving settings differ from the new Swift 1.5 runs, so these are reference comparisons rather than a controlled measurement of the Swift adaptation. Token reductions below are recomputed from those same reference samples.

Benchmark / Swift 1.5 quantization	Qwen base accuracy	Swift 1.5 quant accuracy	Mean token reduction	Median token reduction
GPQA-Diamond AWQ INT4	86.36%	88.38%	↓ 51.5%	↓ 64.4%
GPQA-Diamond AutoRound INT4	86.36%	89.39%	↓ 50.5%	↓ 57.8%
GPQA-Diamond AWQ + GPTQ INT4	86.36%	90.91%	↓ 45.8%	↓ 64.4%
IFBench AWQ INT4	72.00%	72.00%	↓ 36.9%	↓ 49.3%
IFBench AutoRound INT4	72.00%	69.33%	↓ 29.3%	↓ 39.2%
IFBench AWQ + GPTQ INT4	72.00%	70.00%	↓ 31.8%	↓ 52.7%
AIME 2026 AWQ INT4	70.00%	86.67%	↓ 29.2%	↓ 36.2%
AIME 2026 AutoRound INT4	76.67%	83.33%	↓ 17.7%	↓ 32.4%
AIME 2026 AWQ + GPTQ INT4	76.67%	83.33%	↓ 22.4%	↓ 34.0%

AIME scoring: truncated responses count as incorrect for both columns.

The AMD Quark INT4 and FP8 exports have separate sanity evaluations; completed results on these three reasoning benchmarks are not available for them.

Quantized evaluation settings and BF16 reference

Serving: vLLM 0.29.0, tensor parallelism 1, eager execution, BF16 activations, context 131,072, template-default thinking without an effort override. The AWQ + GPTQ export uses FP8 KV cache; BF16, AWQ, and AutoRound use auto KV dtype. Sampling: temperature 1, top-p 0.95, top-k 20, min-p 0, presence penalty 0, repetition penalty 1, seed 0. Output caps: GPQA 100,000, IFBench 81,920, AIME 32,768. IFBench uses official strict prompt-level scoring.

GPQA token counts cover re-tokenized reasoning; IFBench and AIME count the full generated response. Statistics include all responses, including truncations; medians use the midpoint of the two central values when the sample count is even.

Saved Qwen references: W4A16 for GPQA and IFBench; Qwen AWQ for the AWQ AIME row; Qwen W4A16 for the AutoRound and AWQ + GPTQ AIME rows. The latter is a W4A16 reference for AutoRound, not an AutoRound base run. The new runs do not reproduce the original software stack.

The fresh Swift 1.5 BF16 reference and all quantized exports scored as follows under this single-seed protocol:

Model	GPQA-Diamond	IFBench strict	AIME 2026
Swift 1.5 BF16	91.41%	72.00%	86.67%
AWQ INT4	88.38%	72.00%	86.67%
AutoRound INT4	89.39%	69.33%	83.33%
AWQ + GPTQ INT4	90.91%	70.00%	83.33%

Truncation counts are recorded in the linked evaluation data. These single-seed results do not establish quality parity or replace the broader multi-seed evaluation.

Verified counts, token statistics, settings, and evidence hashes.

GGUF quantizations

File	Size	KLD wikitext @512	KLD wikitext @32k	99% KLD @32k	Top-p @32k
Q8_0	29.0 GB	0.0008	0.0006	0.005	98.85%
Q6_K_L	25.0 GB	0.0015	0.0014	0.010	98.16%
Q6_K	23.9 GB	0.0018	0.0016	0.014	98.30%
Q6_K_S	22.9 GB	0.0020	0.0016	0.014	98.24%
Q5_K_M	20.9 GB	0.0052	0.0061	0.050	96.92%
Q5_K_S	19.6 GB	0.0060	0.0069	0.058	96.91%
Q4_K_L	18.8 GB	0.0106	0.0103	0.105	95.79%
Q4_K_M	17.4 GB	0.0137	0.0134	0.163	95.03%
IQ4_NL	17.4 GB	0.0152	0.0140	0.175	95.39%
Q4_K_S	16.4 GB	0.0164	0.0154	0.175	94.84%
IQ4_XS	15.5 GB	0.0179	0.0173	0.187	94.96%
IQ3_M	14.9 GB	0.0410	0.0380	0.409	91.83%
Q3_K_L	14.1 GB	0.0442	0.0410	0.415	91.63%
Q3_K_M	13.4 GB	0.0570	0.0562	0.614	90.30%
IQ3_XS	12.8 GB	0.0583	0.0885	1.130	88.54%
Q3_K_S	12.7 GB	0.0648	0.0658	0.712	89.53%
IQ3_XXS	12.3 GB	0.0742	0.0844	0.996	88.70%
Q2_K	10.8 GB	0.1655	0.1546	1.728	84.00%
IQ2_M	10.5 GB	0.1523	0.1493	1.568	84.17%
IQ2_S	9.7 GB	0.2095	0.2589	3.024	80.58%
IQ2_XS	9.1 GB	0.2433	0.2622	2.951	79.99%
IQ2_XXS	8.9 GB	0.2866	0.2769	2.902	78.48%

Mean KL divergence against the Swift 1.5 BF16 source, lower is better. wikitext @512 is wikitext-2 test, 100 windows of 512 tokens. wikitext @32k is wikitext-2 train, 16 windows of 32,768 tokens, scoring only the last 512 tokens of each window, so every scored token sees at least 32k tokens of context. 99% KLD is the 99th-percentile divergence on the same 32k run. Top-p is top-token agreement with BF16 on the 32k run.

Long context costs very little on this release: for every tier from Q8_0 through Q4_K_S, the 32k mean is within 10% of the 512-token value (Q5_K_M and Q5_K_S rise about 15%), and Q4_K_M keeps the same top token as BF16 on 95% of positions at 32k. The pick below follows the 99th-percentile tail at 32k: 0.163 for Q4_K_M, 0.050 for Q5_K_M, 0.014 for Q6_K, 0.005 for Q8_0.

Use case	Pick
24 GB cards, everyday use	Q4_K_M
Long agentic runs, strict tool-call formatting	Q6_K or higher
Maximum fidelity	Q8_0

Recipe

All 22 tiers were built with llama.cpp commit 6f41ac5 from a BF16 conversion of the published Swift 1.5 safetensors. They reuse the importance matrix and the per-tensor type layouts (--tensor-type-file) that bartowski computed for Swift 1.0 with his quantization-config; Swift 1.5 has the same architecture and tensor shapes. Every file was checked against BF16 on the harness above.

License and access

Swift 1.5 is a derivative of Qwen3.8-27B (Copyright 2026 Alibaba Cloud, Apache License 2.0). UkisAI's contribution, including the adapted weights, is licensed under the Swift Open License v1.0. See NOTICE for the change notice and attribution details.

Personal, research, educational, evaluation, and commercial use are free for individuals and organizations with gross annual 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.

Nothing in the Swift Open License limits rights in Qwen3.8-27B itself under Apache 2.0.

Citation

@misc{swift-1.5-qwen3.8-27b,
  title  = {Swift 1.5 Qwen3.8-27B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b}
}

Acknowledgements

We acknowledge the NVIDIA Innovation Lab, Amazon Web Services, and Google Cloud for providing compute credits and infrastructure support for Swift's development, training, and evaluation.

Downloads last month: 96,510

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for ukisai/Swift-1.5-Qwen3.8-27B-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

ukisai/Swift-1.5-Qwen3.8-27b

Quantized

(49)

this model

Collection including ukisai/Swift-1.5-Qwen3.8-27B-GGUF

Swift 1.5 27B

Collection

Swift 1.5 on Qwen3.8-27B: stronger than Swift 1.0 on agentic and coding tasks, with fewer thinking tokens. BF16 weights and every quant. • 11 items • Updated 8 days ago • 21