quipu-0.6b/README.md

library_name, license, base_model, language, tags

library_name	license	base_model	language	tags
transformers	apache-2.0	Qwen/Qwen3-0.6B	en	lora fine-tuned reasoning lightweight multilingual text-generation conversational instruction-tuned qwen3 small-language-model efficient

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