Instructions to use beyoru/Kiwen1.1-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/Kiwen1.1-27B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="beyoru/Kiwen1.1-27B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("beyoru/Kiwen1.1-27B")
model = AutoModelForMultimodalLM.from_pretrained("beyoru/Kiwen1.1-27B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beyoru/Kiwen1.1-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/Kiwen1.1-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/Kiwen1.1-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/Kiwen1.1-27B

SGLang

How to use beyoru/Kiwen1.1-27B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "beyoru/Kiwen1.1-27B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/Kiwen1.1-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "beyoru/Kiwen1.1-27B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/Kiwen1.1-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/Kiwen1.1-27B with Docker Model Runner:
```
docker model run hf.co/beyoru/Kiwen1.1-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kiwen1.1-27B

Kiwen1.1 is a fine-tuned version of Qwen3.8-27B, trained on long chain-of-thought reasoning traces from Kimi K3 — with a particular focus on reasoning, coding, tool use, and instruction following. But the goal wasn't simply to make the model think longer. The goal was to make it think better.

This model isn't an attempt to build the biggest model.

It's an attempt to make a 27-billion-parameter model think harder, follow instructions better, and act more reliably.

News

Traing focus more in coding task
Improve instruct response of base model

Results

lm-evaluation-harness 0.4.12, chat template applied, max_gen_toks=4096, full datasets. Base measured under the identical harness.

	Qwen3.8-27B	Kiwen-27B	Kiwen1.1-27B
GSM8K strict	67.4	72.78	96.4
GSM8K flexible	74.9	84.38	96.7
IFEval prompt strict	80.4	84.29	83.9
IFEval inst strict	82.5	86.45	87.5
IFEval prompt loose	83.2	86.69	87.2
IFEval inst loose	84.3	88.01	89.7
VMLU val (744)	83.5	86.02	84.8

GSM8K moves by 29 points. That is the headline number and it is real, on the full 1,319-item set. Base and fine-tune were run back to back in the same job so the two columns share their conditions. A second independent run of the fine-tune scored 96.2 / 96.4, which puts the run-to-run spread around 0.3.

VMLU moves much less: +1.3 over the base, against Kiwen-27B's +2.5. The gain is real but small. The training mix is weighted toward reasoning and tool use, not toward Vietnamese factual recall, so a model tuned directly for that recall stays ahead. Base VMLU breaks down as STEM 93.4, Social Science 82.4, Other 78.6, Humanity 74.7, and the humanities gap is where the headroom is.

Internal benchmark

On an internal 20-task suite the model won 5, lost 6 and tied 5, median delta 0.0. The wins are concentrated in classification and routing:

Task	Delta
Translation	+22.9
Intent classification	+20.9
Intent routing	+20.7
Multi-turn tool calling	+15.1

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

m = "beyoru/Kiwen1.1-27B"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, dtype="auto", device_map="auto")

msgs = [{"role": "user", "content": "Natalia sold clips to 48 friends in April, "
                                     "and half as many in May. How many total?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True,
                              enable_thinking=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=4096)[0][ids.shape[-1]:]))

Set enable_thinking=False for extraction, classification and formatting tasks. The model was trained with both modes and respects the flag.

Serving with SGLang:

python -m sglang.launch_server --model-path beyoru/Kiwen1.1-27B \
  --context-length 262144

Citation

@misc{kiwen27bk3,
  title  = {Kiwen1.1-27B},
  author = {beyoru},
  year   = {2026},
  url    = {https://huggingface.co/beyoru/Kiwen1.1-27B}
}

License & Attribution

Built on:

Qwen3.8-27B — Apache-2.0

Kiwen1.1-27B is released under the Apache-2.0 license.

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Safetensors

Model size

28B params

Tensor type

BF16

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