Instructions to use Sebastianpro88/Chichu-2.5-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sebastianpro88/Chichu-2.5-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="Sebastianpro88/Chichu-2.5-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sebastianpro88/Chichu-2.5-Reasoning")
model = AutoModelForCausalLM.from_pretrained("Sebastianpro88/Chichu-2.5-Reasoning", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sebastianpro88/Chichu-2.5-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sebastianpro88/Chichu-2.5-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sebastianpro88/Chichu-2.5-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sebastianpro88/Chichu-2.5-Reasoning

SGLang

How to use Sebastianpro88/Chichu-2.5-Reasoning 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 "Sebastianpro88/Chichu-2.5-Reasoning" \
    --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": "Sebastianpro88/Chichu-2.5-Reasoning",
		"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 "Sebastianpro88/Chichu-2.5-Reasoning" \
        --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": "Sebastianpro88/Chichu-2.5-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sebastianpro88/Chichu-2.5-Reasoning with Docker Model Runner:
```
docker model run hf.co/Sebastianpro88/Chichu-2.5-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chichu 2.5 Reasoning 🐱🧠

A 500M parameter language model with chain-of-thought reasoning using <think> blocks. Fine-tuned from Chichu 2.0 on reasoning data covering math, logic, code, and general questions.

Named after Chichu the cat. 🐱

What's New vs Chichu 2.0

Chichu 2.5 uses <think>...</think> blocks to reason through problems step by step before answering — similar to DeepSeek-R1 and Kimi-K3.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("Sebastianpro88/Chichu-2.5-Reasoning", torch_dtype=torch.float16, device_map="cpu")
tokenizer = AutoTokenizer.from_pretrained("Sebastianpro88/Chichu-2.5-Reasoning")

messages = [
    {"role": "system", "content": "You are Chichu 2.5. Use <think> blocks to reason before answering."},
    {"role": "user", "content": "What is 15% of 240?"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

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Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for Sebastianpro88/Chichu-2.5-Reasoning

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

Sebastianpro88/Chichu-2.0-500M-Instruct

Finetuned

(1)

this model

Quantizations

1 model