Training Details
- Base model: Elinnos V4 merged (Qwen2.5-7B-Instruct + I2C + multi-IP peripherals)
- LoRA config: r=32, alpha=64, dropout=0.05, 7 target modules
- Dataset: 600 AHB SRAM samples (540 train / 60 val) from
sram_all.jsonl
- Training: 3 epochs, lr=1e-4, bf16, gradient checkpointing, assistant-only loss
- Eval token accuracy: 99.71%
- Eval loss: 0.0077
- Training time: ~49 minutes on NVIDIA RTX A6000
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base_model = "Elinnos/elinnos-v4-merged"
adapter_model = "Elinnos/elinnos-qwen2.5-7b-sram-lora"
tokenizer = AutoTokenizer.from_pretrained(adapter_model)
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_model)
Training procedure
This model was trained with SFT.
Framework versions
- PEFT 0.19.1
- TRL: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.4
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}