Tini-8B-A1B

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.

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📊 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

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🛠️ 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

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🏃‍♂️ 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

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
    )