metadata
license: mit
datasets:
- glaiveai/glaive-function-calling-v2
- nickrosh/Evol-Instruct-Code-80k-v1
language:
- en
base_model:
- LiquidAI/LFM2.5-1.2B-Instruct
tags:
- tool-use
- code
- unsloth
- liquid
- fine-tune
library_name: unsloth
🧠 LFM-2.5-1.2B-Coding-Tools
This is a fine-tuned version of Liquid LFM-2.5-1.2B-Instruct, specialized for Python coding and native tool calling. It was trained using Unsloth on a hybrid dataset of coding instructions and Pythonic function calls.
📉 Training Results & Metrics
This model was fine-tuned on a Google Colab Tesla T4 instance. The following metrics were recorded during the final training run.
| Metric | Value | Description |
|---|---|---|
| Final Loss | 0.7431 |
The model's error rate at the final step. |
| Average Train Loss | 0.8274 |
The average error rate across the entire session. |
| Epochs | 0.96 |
Completed ~1 full pass over the dataset. |
| Global Steps | 60 |
Total number of optimizer updates. |
| Runtime | 594s (~10 min) |
Total wall-clock time for training. |
| Samples/Second | 0.808 |
Throughput speed on T4 GPU. |
| Gradient Norm | 0.345 |
Indicates stable training (no exploding gradients). |
| Learning Rate | 3.64e-6 |
Final learning rate after decay. |
| Total FLOS | 2.07e15 |
Total floating-point operations computed. |
🛠️ Hardware & Framework
- Hardware: NVIDIA Tesla T4 (Google Colab Free Tier)
- Framework: Unsloth (PyTorch)
- Quantization: 4-bit (QLoRA)
- Optimizer: AdamW 8-bit
View Raw Training Log (JSON)
{
"_runtime": 348,
"_step": 60,
"_timestamp": 1770910365.0772636,
"_wandb.runtime": 348,
"total_flos": 2069937718053888,
"train/epoch": 0.96,
"train/global_step": 60,
"train/grad_norm": 0.3452725112438202,
"train/learning_rate": 0.000003636363636363636,
"train/loss": 0.7431,
"train_loss": 0.8273822158575058,
"train_runtime": 594.2969,
"train_samples_per_second": 0.808,
"train_steps_per_second": 0.101
}