Instructions to use Dang/qagenie-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dang/qagenie-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Dang/qagenie-ft") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Model Details
Model Description
- Model type: Large Language Model
- Finetuned from model [optional]: qwen2.5:7b
Model Sources [optional]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
- EC2 g6 instance with 48 VRAM
Framework versions
- PEFT 0.15.2
- Downloads last month
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Model tree for Dang/qagenie-ft
Base model
Qwen/Qwen2.5-7B Finetuned
Qwen/Qwen2.5-7B-Instruct