Instructions to use hara-CU/Advanced_FinalCandidate_482 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hara-CU/Advanced_FinalCandidate_482 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-4B-Instruct-2507 | |
| datasets: | |
| - hara-CU/LLM2025_DB_base_AW_345NoEAd_ALFformat_QH5L4R5_1392 | |
| language: | |
| - en | |
| license: mit | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| tags: | |
| - lora | |
| - agent | |
| - tool-use | |
| - alfworld | |
| - dbbench | |
| # Advanced_FinalCandidate_482 | |
| This repository contains the **full-merged 16-bit weights** fine-tuned from | |
| **Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**. | |
| No adapter loading is required. | |
| ## Usage | |
| Since this is a merged model, you can use it directly with `transformers`. | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| model_id = "hara-CU/Advanced_FinalCandidate_482" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| ) | |
| ``` | |
| ## Sources & Terms (IMPORTANT) | |
| Training data: hara-CU/LLM2025_DB_base_AW_345NoEAd_ALFformat_QH5L4R5_1392 | |
| Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. | |
| Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use. | |