Instructions to use josephmayo/Holo-3.1-4B-Coder-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use josephmayo/Holo-3.1-4B-Coder-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Hcompany/Holo-3.1-4B") model = PeftModel.from_pretrained(base_model, "josephmayo/Holo-3.1-4B-Coder-LoRA") - Notebooks
- Google Colab
- Kaggle
| { | |
| "root": "/kaggle/input/datasets/josephayanda/curated-agent-coding-dataset-sft-v1", | |
| "used": [ | |
| { | |
| "file": "targeted_sft_2000.jsonl", | |
| "available": 2000, | |
| "selected": 2000, | |
| "purpose": "targeted failure families" | |
| }, | |
| { | |
| "file": "eval_maxxing_3500.jsonl", | |
| "available": 3500, | |
| "selected": 3200, | |
| "purpose": "hidden-test style coding" | |
| }, | |
| { | |
| "file": "coding_sft_5000.jsonl", | |
| "available": 5000, | |
| "selected": 3200, | |
| "purpose": "broad language coding" | |
| }, | |
| { | |
| "file": "real_world_coding_3500.jsonl", | |
| "available": 3500, | |
| "selected": 1800, | |
| "purpose": "production coding tasks" | |
| }, | |
| { | |
| "file": "heavy_real_world_agentic_5000.jsonl", | |
| "available": 5000, | |
| "selected": 120, | |
| "purpose": "long repo-agent examples" | |
| } | |
| ], | |
| "skipped_preference_pairs": [ | |
| "orpo_gold_1000.jsonl", | |
| "reasoning_preference_1000.jsonl" | |
| ], | |
| "external": [ | |
| { | |
| "dataset": "ise-uiuc/Magicoder-Evol-Instruct-110K", | |
| "selected": 96 | |
| }, | |
| { | |
| "dataset": "m-a-p/CodeFeedback-Filtered-Instruction", | |
| "selected": 96 | |
| }, | |
| { | |
| "dataset": "HuggingFaceH4/CodeAlpaca_20K", | |
| "selected": 96 | |
| }, | |
| { | |
| "dataset": "glaiveai/glaive-code-assistant-v3", | |
| "selected": 96 | |
| } | |
| ] | |
| } |