Instructions to use Rumiii/Qwimi-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rumiii/Qwimi-4B with Transformers:

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

pipe = pipeline("text-generation", model="Rumiii/Qwimi-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rumiii/Qwimi-4B")
model = AutoModelForCausalLM.from_pretrained("Rumiii/Qwimi-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rumiii/Qwimi-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rumiii/Qwimi-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rumiii/Qwimi-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rumiii/Qwimi-4B

SGLang

How to use Rumiii/Qwimi-4B 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 "Rumiii/Qwimi-4B" \
    --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": "Rumiii/Qwimi-4B",
		"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 "Rumiii/Qwimi-4B" \
        --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": "Rumiii/Qwimi-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rumiii/Qwimi-4B with Docker Model Runner:
```
docker model run hf.co/Rumiii/Qwimi-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B-Instruct-2507 Distilled from Kimi K2

This is a repository which hosts the model files of the Model Distillation performed from the parent model Kimi K2 to the student model Qwen3-4B-Instruct-2507, with the aim of further advancing the student's agentic capabilities, particularly multi-step tool calling.

Model Overview


Parent (teacher) model	Kimi K2
Student model	Qwen3-4B-Instruct-2507
Dataset	Agent-Ark/Toucan-1.5M (Kimi-K2 configuration)
Method	Supervised fine-tuning on teacher trajectories

Training Data

Toucan-1.5M contains over 1.5 million agentic trajectories synthesized from 495 real-world Model Context Protocol (MCP) servers, spanning more than 2,000 tools. It includes single-turn and multi-turn interactions, as well as sequential and parallel tool calls with real tool execution.

Training used the Kimi-K2 configuration of the dataset. A total of 9,168 samples were used for a single epoch, taken from all four subsets in the following distribution:

Subset	Samples	Share
single-turn-original	2,750	30%
single-turn-diversify	2,292	25%
irrelevant	1,375	15%
multi-turn	2,751	30%

How to use it

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Rumiii/Qwimi-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="cuda"
)

messages = [
    {"role": "user", "content": "What is the capital of France?"}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Only a portion of the full dataset was used. Samples longer than 4,096 tokens were excluded (no sample was truncated), as were multi-turn samples that contain tool calls but no declared tools. The trajectories follow the standard Qwen3 tool-calling chat template.

Note

This distillation was performed through an existing dataset. The Kimi K2 responses were not generated live for this model; the trajectories come from Agent-Ark/Toucan-1.5M, which was already available on Hugging Face.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Rumiii/Qwimi-4B

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(2729)

this model

Quantizations

3 models

Rumiii
/

Qwimi-4B