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 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
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 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.