Instructions to use Quipuai/quipu-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quipuai/quipu-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="Quipuai/quipu-0.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Quipuai/quipu-0.6b")
model = AutoModelForCausalLM.from_pretrained("Quipuai/quipu-0.6b", 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 Quipuai/quipu-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quipuai/quipu-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Quipuai/quipu-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Quipuai/quipu-0.6b

SGLang

How to use Quipuai/quipu-0.6b 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 "Quipuai/quipu-0.6b" \
    --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": "Quipuai/quipu-0.6b",
		"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 "Quipuai/quipu-0.6b" \
        --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": "Quipuai/quipu-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Quipuai/quipu-0.6b with Docker Model Runner:
```
docker model run hf.co/Quipuai/quipu-0.6b
```

Quipu 0.6B

A compact, fast fine-tuned language model built on Qwen3-0.6B, tuned for clear step-by-step reasoning, consistent identity, and lightweight coding assistance.

Designed to punch above its weight class: at just 0.6B parameters, Quipu runs fast and cheap while staying focused on giving structured, logical answers — a solid pick when you need a responsive assistant without the overhead of a much larger model.

Good for

Step-by-step reasoning and simple logic problems
Basic coding help (short functions, quick snippets)
Fast, low-resource deployment (edge devices, quick prototyping, local inference)

Model Details

Base model: Qwen/Qwen3-0.6B
Fine-tuning method: LoRA
Languages: English, Spanish
License: Apache 2.0

How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Quipuai/quipu-0.6b")
tokenizer = AutoTokenizer.from_pretrained("Quipuai/quipu-0.6b")

messages = [{"role": "user", "content": "Who are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

GGUF / Ollama

Quantized GGUF versions (compatible with Ollama, llama.cpp, LM Studio, etc.) are available thanks to mradermacher:

mradermacher/quipu-0.6b-GGUF — standard quants
mradermacher/quipu-0.6b-i1-GGUF — imatrix quants (better quality)

To run with Ollama:

ollama run hf.co/mradermacher/quipu-0.6b-GGUF:Q4_K_M

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Safetensors

Model size

0.6B params

Tensor type

F16

Model tree for Quipuai/quipu-0.6b

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Adapter

(581)

this model

Adapters

2 models