Instructions to use hetp3042/agentedu-mistral-7b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hetp3042/agentedu-mistral-7b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") model = PeftModel.from_pretrained(base_model, "hetp3042/agentedu-mistral-7b-lora") - Notebooks
- Google Colab
- Kaggle
AgentEdu-Mistral-7B LoRA
Fine-tuned LoRA adapter for Mistral-7B-Instruct-v0.3, trained on educational Q&A data for the AgentEdu AI Teaching Platform.
Model Details
- Base Model:
mistralai/Mistral-7B-Instruct-v0.3 - LoRA Rank (r): 32
- LoRA Alpha: 64
- Dropout: 0.05
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Task: Causal Language Modeling (Teaching & Educational Q&A)
Usage
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load base model
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "hetp3042/agentedu-mistral-7b-lora")
Part of AgentEdu
This model powers the AI Subject Expert agents in the AgentEdu platform — a multi-agent AI teaching system with 38 specialized agents and 9 AI models.
- Downloads last month
- -
Model tree for hetp3042/agentedu-mistral-7b-lora
Base model
mistralai/Mistral-7B-v0.3 Finetuned
mistralai/Mistral-7B-Instruct-v0.3