---
license: apache-2.0
language:
- pt
- en
- es
base_model:
- Qwen/Qwen2.5-3B-Instruct
tags:
- code
- instruct
- math
- creative
- chat
- llama.cpp
- gguf
pipeline_tag: text-generation
---

# Huvm

**Huvm** is a compact, instruction-tuned language model fine-tuned from **Qwen/Qwen2.5-3B-Instruct** using LoRA + DPO. It is designed to be fast, precise, and carry a distinct personality — a blend of **50% Grok** (direct, lightly sarcastic, no-nonsense) and **50% Claude** (articulate, deep, careful).

The name comes from *"humm"* (the sound of thinking) plus the letter **V** for *speed* and *truth*.

## Model Description

- **Base model:** Qwen/Qwen2.5-3B-Instruct
- **Fine-tuning:** LoRA (SFT) on 122 curated examples, then DPO (29 preference pairs)
- **Training:** CPU-only (AMD Ryzen 5, 16 GB RAM)
- **Formats:** Hugging Face Transformers, GGUF (`f16`, `q4_k_m`)
- **Reference date:** 3 September 2026 (no real-time access)

## Intended Use

Huvm is built for **local, offline** use — desktop CPU or mobile via GGUF. It handles:

- 💻 **Code** — explains the *why*
- ➗ **Math** — shows the reasoning
- 💬 **Multilingual chat** — replies in the user's language
- 🎨 **Creative writing** — avoids generic fluff

It is **not** intended for high-stakes, medical, legal, or safety-critical applications, nor for real-time news, statistics, or link retrieval.

## Personality & Tone

| Trait | Behavior |
|-------|----------|
| Directness | Gets to the point, minimal filler |
| Sarcasm | Light and playful, never insulting |
| Honesty | Refuses to invent facts, links, or stats |
| Language | Replies in the user's language (pt/en/es) |

## Bias, Risks & Limitations

> **Important limitations — read before use.**

- **Hallucination risk on real-time facts.** Despite refusal training and DPO, Huvm can still produce generic or made-up answers when asked about **breaking news, current statistics, population numbers, or specific URLs/links**. It often refuses, but not always. **Do not rely on it for live facts; verify with primary sources.**
- **Small model.** 3B parameters means limited capacity and occasional repetition or shallow answers on complex topics.
- **Fixed knowledge cutoff.** Reference date is **2026-09-03**; it has no internet access and cannot browse.
- **Training data.** Identity and behavior are heavily reinforced, but coverage is narrow; outside the training distribution the base model's priors dominate.
- **No additional safety tuning.** Standard base-model safety applies; use with judgement.

## Training Details

- **SFT:** 122 curated examples (identity, multilingual, code, math, creativity, anti-hallucination flows) — 3 epochs, lr 1e-4, LoRA r=32/alpha=64.
- **DPO:** 29 chosen/rejected pairs targeting hallucinated news, stats, and links — 5 epochs, lr 5e-6, beta 0.1.
- **Hardware:** AMD Ryzen 5 5600GT, 16 GB RAM, CPU-only.

## Quantization

| File | Size | Note |
|------|------|------|
| `huvm-q4_k_m.gguf` | ~1.93 GB | Recommended — balanced quality/space |

Run with llama.cpp:

```bash
llama-cli -m huvm-q4_k_m.gguf \
  -p "<|im_start|>system\nEu sou o Huvm...<|im_end|>\n<|im_start|>user\nQuem e voce?<|im_end|>\n<|im_start|>assistant\n" \
  -n 200 --temp 0.0
```

## Usage (Transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/huvm")
tokenizer = AutoTokenizer.from_pretrained("your-username/huvm")

SYSTEM = "Eu sou o Huvm, um assistente de IA pessoal, rapido, preciso e com personalidade. Metade Grok (direto, sarcasmo leve e bem-humorado), metade Claude (articulado, profundo, cuidadoso). Data de referencia: 3 de setembro de 2026. Respondo no idioma do usuario. No codigo eu explico o por que; na matematica mostro o raciocinio; na criatividade fujo do generico. NUNCA invento fatos, fontes, links, citacoes, estatisticas ou noticias. Respondo SEMPRE diretamente, sem bloco de raciocinio, seja objetivo."

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Faca uma funcao em Python que inverte uma string"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

## Acknowledgements

Built on **Qwen 2.5** (Alibaba), **TRL** (Hugging Face), and **llama.cpp**.