Instructions to use KittyLM/kittylm-qwen3-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KittyLM/kittylm-qwen3-4b with Transformers:

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

pipe = pipeline("text-generation", model="KittyLM/kittylm-qwen3-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KittyLM/kittylm-qwen3-4b")
model = AutoModelForCausalLM.from_pretrained("KittyLM/kittylm-qwen3-4b", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KittyLM/kittylm-qwen3-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KittyLM/kittylm-qwen3-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KittyLM/kittylm-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KittyLM/kittylm-qwen3-4b

SGLang

How to use KittyLM/kittylm-qwen3-4b 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 "KittyLM/kittylm-qwen3-4b" \
    --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": "KittyLM/kittylm-qwen3-4b",
		"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 "KittyLM/kittylm-qwen3-4b" \
        --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": "KittyLM/kittylm-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KittyLM/kittylm-qwen3-4b with Docker Model Runner:
```
docker model run hf.co/KittyLM/kittylm-qwen3-4b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KittyLM-4B (qwen3-4b)

A kitten. In a language model. This is a LoRA finetune of Qwen/Qwen3-4B that answers everything in kitten language (mrrp, nya~, prrr, *actions*, occasional :3) while staying factually correct underneath.

Training

Data: 900 ShareGPT-style pairs + 100 held-out eval (see KittyLM/kittylm-data), system prompt baked in
Method: LoRA SFT (scripts/train.py in the KittyLM project), RTX 3060 12GB
Files: merged bf16 weights + the LoRA adapter (adapter_*.safetensors) live side by side. AutoModelForCausalLM loads the merged model; PeftModel picks up the adapter.
GGUF quants for Ollama / llama.cpp / LM Studio: KittyLM/kittylm-qwen3-4b-gguf
Eval: v2 (prompt-free): ablation full/none/generic style 0.75/0.78/0.81, 15/15 factual; train loss 5.74->0.48, eval loss 0.885->0.869->0.937 (best @ epoch 2). Voice e.g. 'plant drinks sunlight... solar dinner buffet. envy. owo.'

Limitations

Persona is stylistic, not a refusal behavior: an explicit "answer in plain English" can make it drop character — it was never trained to resist.
Small-model knowledge gaps persist: off-distribution facts may confabulate (tracked per model by the 20-probe suite in eval/).
Kitten flavor adds no capability: reasoning/coding limits are the base model's limits.

Usage (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("KittyLM/kittylm-qwen3-4b")
model = AutoModelForCausalLM.from_pretrained("KittyLM/kittylm-qwen3-4b", device_map="auto", dtype="bfloat16")
msgs = [{"role": "user", "content": "What's the capital of Japan?"}]
x = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
print(tok.decode(model.generate(**tok(x, return_tensors="pt").to(model.device), max_new_tokens=120)[0]))
# mrrp... Tokyo. big city. lots of cats. nya~

Usage (Ollama)

hf download KittyLM/kittylm-qwen3-4b-gguf --include "*.gguf" --local-dir ./gguf
ollama create kittylm-qwen3-4b -f Modelfile   # see Modelfile template in project
ollama run kittylm-qwen3-4b "Good night!"