Instructions to use ming9999/ATLAS-Qwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ming9999/ATLAS-Qwen2.5-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B") model = PeftModel.from_pretrained(base_model, "ming9999/ATLAS-Qwen2.5-7B") - Notebooks
- Google Colab
- Kaggle
File size: 819 Bytes
790b7f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"anchor_mode": "true",
"base_model": "Qwen/Qwen2.5-7B",
"batch_size": 4,
"bce_tau": 12.0,
"epochs": 5,
"grad_accum": 8,
"grad_ckpt": true,
"grad_clip": 1.0,
"human_class": false,
"lam_anchor": 0.0,
"lam_disc": 0.0,
"lam_hum": 0.0,
"lam_marg": 0.0,
"lam_rej": 0.0,
"lam_sft": 0.3,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_r": 16,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj"
],
"loss": "ce",
"lr": 0.0001,
"marg_T": 1.0,
"marg_detach": false,
"max_len": 512,
"per_class": 0,
"per_source_lora_b": true,
"prefix_len": 1,
"rej_beta": 0.2,
"rej_mode": "pair",
"seed": 42,
"sources": [
"gpt",
"claude",
"gemini",
"mistral",
"llama"
],
"tau": 1.0,
"warmup_ratio": 0.05,
"weight_decay": 0.01
} |