Instructions to use codewithdark/mlpr-qwen2.5-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use codewithdark/mlpr-qwen2.5-7b-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "codewithdark/mlpr-qwen2.5-7b-instruct") - Notebooks
- Google Colab
- Kaggle
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Download README.md from codewithdark/mlpr-qwen2.5-7b-instruct: direct link, hf CLI and curl.
- Browser
- Download file 1.55 kB
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https://huggingface.co/codewithdark/mlpr-qwen2.5-7b-instruct/resolve/main/README.md
- Command line
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hf download hf://codewithdark/mlpr-qwen2.5-7b-instruct/README.md
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curl -L -o README.md https://huggingface.co/codewithdark/mlpr-qwen2.5-7b-instruct/resolve/main/README.md
1.55 kB
metadata
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: mlpr-qwen2.5-7b-instruct
results: []
mlpr-qwen2.5-7b-instruct
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.9692
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2157 | 1.0 | 75 | 2.9303 |
| 0.6064 | 2.0 | 150 | 3.9398 |
| 0.6039 | 3.0 | 225 | 3.9692 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1