Instructions to use Sebastianpro88/Chichu-1.5-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sebastianpro88/Chichu-1.5-Flash with Transformers:

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

pipe = pipeline("text-generation", model="Sebastianpro88/Chichu-1.5-Flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sebastianpro88/Chichu-1.5-Flash")
model = AutoModelForCausalLM.from_pretrained("Sebastianpro88/Chichu-1.5-Flash", 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-1.5-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sebastianpro88/Chichu-1.5-Flash"
# 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-1.5-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sebastianpro88/Chichu-1.5-Flash

SGLang

How to use Sebastianpro88/Chichu-1.5-Flash 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-1.5-Flash" \
    --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-1.5-Flash",
		"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-1.5-Flash" \
        --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-1.5-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sebastianpro88/Chichu-1.5-Flash with Docker Model Runner:
```
docker model run hf.co/Sebastianpro88/Chichu-1.5-Flash
```

Chichu-1.5-Flash / README.md

Sebastianpro88

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	---
	language:
	- en
	license: other
	library_name: transformers
	tags:
	- chat
	- conversational
	- distillation
	- reasoning
	- code
	- chichu
	base_model: HuggingFaceTB/SmolLM2-135M-Instruct
	pipeline_tag: text-generation
	---

	# Chichu 1.5 Flash 🐱

	A small, fast language model fine-tuned from SmolLM2-135M-Instruct on the
	[r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation](https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
	dataset — a multi-teacher distillation corpus covering math, code, reasoning,
	instruction-following, and tool-use.

	Named after Chichu the cat. 🐱

	## Model Details

	- Base model: HuggingFaceTB/SmolLM2-135M-Instruct
	- Parameters: 135M (1.84M LoRA adapters trained)
	- Training: LoRA fine-tuning (rank=16, alpha=32) on 4 shards of the distillation dataset
	- LoRA targets: q_proj, k_proj, v_proj, o_proj
	- Identity: Enforced via system prompt (see usage below)

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"Sebastianpro88/Chichu-1.5-Flash",
	torch_dtype=torch.float16,
	device_map="cpu"
	)
	tokenizer = AutoTokenizer.from_pretrained("Sebastianpro88/Chichu-1.5-Flash")

	system_prompt = (
	"You are Chichu 1.5 Flash, a fast and capable language model named after Chichu the cat. "
	"You are helpful, concise, and friendly."
	)

	messages = [
	{"role": "system", "content": system_prompt},
	{"role": "user", "content": "What is your name?"}
	]

	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt")
	with torch.no_grad():
	out = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
	print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	# "My name is Chichu 1.5 Flash!"
	```

	## Capabilities

	- ✅ Code generation (Python, JavaScript)
	- ✅ Math and reasoning
	- ✅ Instruction following
	- ✅ Conversational responses
	- ✅ Knows its name (via system prompt)

	## Training Data

	Trained on the [qwen3.8-max-glm5.2-kimi-k3-distillation](https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
	dataset — 57,937 multi-teacher distillation traces from Qwen3.8-Max, GLM-5.2, and Kimi-K3
	across math, code, reasoning, instruction, and agent_tool domains.

	## License

	This model inherits the license of the base SmolLM2 model and the distillation dataset.