🚀 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 |
| Base Model | LiquidAI/LFM2.5-2.6B |
| Fine-Tuning Framework | 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
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,
)