Instructions to use ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation with Transformers:

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

pipe = pipeline("image-text-to-text", model="ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation")
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("ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation")
model = AutoModelForMultimodalLM.from_pretrained("ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation", 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 ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation",
		"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/ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation

SGLang

How to use ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation 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 "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation" \
    --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": "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation",
		"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 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 "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation" \
        --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": "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation",
    max_seq_length=2048,
)

Docker Model Runner
How to use ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation with Docker Model Runner:
```
docker model run hf.co/ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.5-2b-Kimi-and-Opus-Distillation

This model is a distilled version of Qwen 3.5 (2B), fine-tuned using high-quality reasoning and conversational datasets. The distillation process leverages responses from Kimi 2.5 and Claude 4.6 Opus to enhance the reasoning capabilities and conversational depth of the lightweight 2B parameter model.

Model Details

Developed by: ertghiu256
Base Model: Qwen/Qwen3.5-2B
Language(s): English
License: Apache 2.0
Finetuning Technique: Supervised Fine-Tuning (SFT) / Distillation

Training Metadata

The model was trained with an emphasis on high-quality data density rather than sheer volume, focusing on a specific subset of data to optimize performance within a short training window.

Training Hardware: 1x T4 (Google Colab/GCP)
Total Training Time: ~2 Hours
Max Training Steps: 70
Learning Rate: $1 \times 10^{-4}$ (1e-4)

Dataset Composition

The model was trained on a curated mixture of the following three datasets:

Ali-Yaser/KIMI-K2.5-450000x-ShareGPT: Large-scale conversational data distilled from Kimi, providing natural, helpful, and long-context-aware dialogue.
allenai/tulu-3-sft-mixture: A diverse, high-quality SFT mixture designed to improve general instruction-following capabilities.
nohurry/Opus-4.6-Reasoning-3000x-filtered: A highly filtered set of complex reasoning chains distilled from Claude 4.6 Opus, designed to improve the model's logical "Chain of Thought" (CoT) processes.

Intended Use

Reasoning-heavy tasks: Despite its size, the model is tuned to handle logical queries better than the base 2B.
Mobile/Edge Deployment: Due to its 2B parameter count, it is ideal for local-first applications.
Conversational AI: High-quality dialogue based on Kimi and Opus styles.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the importance of distillation in small language models."
messages = [
    {"role": "system", "content": "You are a helpful and logical assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Limitations and Biases

While the distillation from Opus and Kimi provides a significant boost, the model's 2B parameter size still limits its "world knowledge" compared to larger models. Users should verify factual claims. The model may inherit biases present in the distillation source datasets.

Uploaded finetuned model

Developed by: ertghiu256
License: apache-2.0
Finetuned from model : unsloth/Qwen3.5-2B

This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 55

Safetensors

Model size

2B params

Tensor type

F32

BF16

Model tree for ertghiu256/Qwen3.5-2b-Kimi-and-Opus-Distillation

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

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Quantizations

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Qwen3.5-2b-Kimi-and-Opus-Distillation