Instructions to use S4MPL3BI4S/gemma4-e4b-openclaw-agent-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use S4MPL3BI4S/gemma4-e4b-openclaw-agent-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-e4b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "S4MPL3BI4S/gemma4-e4b-openclaw-agent-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -25,7 +25,7 @@ This repository contains a LoRA adapter fine-tuned from `unsloth/gemma-4-E4B-it`
|
|
| 25 |
- **Max sequence length:** `2048`
|
| 26 |
- **Batching:** per-device batch size `1`, gradient accumulation `4`
|
| 27 |
- **Learning rate:** `0.0002`
|
| 28 |
-
- **Training steps:** `
|
| 29 |
- **Seed:** `3407`
|
| 30 |
|
| 31 |
## Data Formatting
|
|
|
|
| 25 |
- **Max sequence length:** `2048`
|
| 26 |
- **Batching:** per-device batch size `1`, gradient accumulation `4`
|
| 27 |
- **Learning rate:** `0.0002`
|
| 28 |
+
- **Training steps:** `1000`
|
| 29 |
- **Seed:** `3407`
|
| 30 |
|
| 31 |
## Data Formatting
|