---
base_model: LiquidAI/LFM2.5-8B-A1B
library_name: transformers
model_name: Tini-8B-A1B
tags:
- generated_from_trainer
- sft
- unsloth
- trl
- reasoning
- agentic
- function-calling
licence: mit
pipeline_tag: text-generation
dataset:
- nohurry/Opus-4.6-Reasoning-3000x-filtered
- Jackrong/DeepSeek-V4-Distill-8000x
- Jackrong/Qwen3.5-reasoning-700x
- NousResearch/hermes-function-calling-v1
---

# Tini-8B-A1B

<p align="center">
  <img src="thumbnail.png" width="600" alt="Tini-8B-A1B Logo">
</p>

Tini-8B-A1B is a fine-tuned version of the hybrid model architecture LiquidAI/LFM2.5-8B-A1B. This model is optimized for Agentic Reasoning, seamlessly combining deep chain-of-thought (CoT), native system function calling capabilities.

---

## 📊 Dataset Mixture

The model was Supervised Fine-Tuned (SFT) on a curated mixture of samples balancing deep reasoning and function-calling actions:

| Dataset | Category |
| :--- | :--- |
| `nohurry/Opus-4.6-Reasoning-3000x-filtered` | Advanced Reasoning |
| `Jackrong/DeepSeek-V4-Distill-8000x` | Reasoning / Math / Code |
| `Jackrong/Qwen3.5-reasoning-700x` | Logic / Hard Math |
| `NousResearch/hermes-function-calling-v1` | Tool Use / Agentic |

---

## 🛠️ Training Techniques

To preserve the model's core capabilities while focusing gradient updates entirely on reasoning tracks, the following configurations were applied:

- **Train on Response Only**
- **LoRA Target Modules**

---

## 🏃‍♂️ Quick Start & Inference Parameters Guide

### 💡 Recommended Decoding Parameters
* **General & Contextual Reasoning (Riddles, Nuances, Analysis):**
  `temperature: 0.6` | `top_p: 0.95` | `top_k: 50` | `repetition_penalty: 1.10`

* **Mathematics & Technical Coding Tasks:**
  `temperature: 0.35` | `top_p: 0.90` | `top_k: 40` | `repetition_penalty: 1.08`

### 🚀 Python Example Script
```python
import torch
from unsloth import FastLanguageModel
from transformers import TextStreamer

MODEL_PATH = "./Tini-8B-A1B"

# 1. Load model with 4-bit quantization for VRAM efficiency
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = MODEL_PATH,
    max_seq_length = 2048,
    dtype = torch.bfloat16,
    load_in_4bit = True,
    trust_remote_code = True
)
FastLanguageModel.for_inference(model)

# 2. Set system prompt forcing Vietnamese internal monologue
messages = [
    {
        "role": "system", 
        "content": "Bạn là một trợ lý AI thông minh. BẮT BUỘC phải thực hiện toàn bộ chuỗi suy luận trong thẻ <think> bằng TIẾNG VIỆT để bảo toàn ngữ cảnh văn hóa và tiết kiệm token."
    },
    {
        "role": "user", 
        "content": "Một bể nước đang cạn hoàn toàn. Nếu mở riêng vòi A đầy sau 4 giờ. Mở riêng vòi B (vòi xả) cạn sau 6 giờ. Hỏi nếu mở cả hai vòi cùng lúc thì sau bao lâu đầy được 75% bể?"
    }
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt=True)

# 3. Generate with streaming output and a 2048 max token limit
with torch.no_grad():
    _ = model.generate(
        input_ids = inputs,
        streamer = text_streamer,
        max_new_tokens = 2048,
        use_cache = True,
        temperature = 0.6,
        top_p = 0.95,
        top_k = 50,
        repetition_penalty = 1.10
    )
```