---
base_model: LiquidAI/LFM2.5-2.6B
library_name: transformers
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2.5
- code
- reasoning
- gguf
license: apache-2.0
language:
- en
datasets:
- Banaxi-Tech/Deepseek-V4-Reasoning-Code-2500
---

# 🚀 LFM-2.5-Coder-2.6B

An enhanced, lightweight code-reasoning model built on top of **LiquidAI's LFM-2.5-2.6B**. Fine-tuned to boost step-by-step reasoning and coding capabilities without sacrificing overall model performance.

---

## 📌 Quick Summary

| Feature | Details |
| :--- | :--- |
| **Developed by** | [Schnuckade](https://huggingface.co/Schnuckade) |
| **Base Model** | [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) |
| **Fine-Tuning Framework** | [Unsloth](https://github.com/unslothai/unsloth) 🦥 |
| **Dataset Used** | `Banaxi-Tech/Deepseek-V4-Reasoning-Code-2500` |
| **License** | Apache-2.0 |
| **Language** | English |

---

## 💡 About the Model

**LFM-2.5-Coder-2.6B** is a targeted, lightweight fine-tune designed to make the original Liquid AI base model a bit smarter when dealing with code generation and problem-solving. 

* 🧠 **Reasoning Focus:** Helps the model "think" through coding problems step-by-step.
* ⚡ **Lightweight & Efficient:** Trained specifically to boost coding skills while retaining general utility.
* 📦 **Multi-Format:** Includes full FP16/BF16 safetensors alongside quantized GGUF weights (`Q4_K_M`) for easy local deployment.

---

## 🛠️ Usage & Integration

### Transformers / Unsloth

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Schnuckade/LFM-2.5-Coder-2.6B",
    max_seq_length = 2048,
    load_in_4bit = True,
)