Dapinsky/PIT-4B-FT-202212-math-dapo-lora
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Dapinsky/PIT-4B-FT-202212-math-dapo-lora
This repo contains a PEFT LoRA adapter trained from Diamegs/PIT-4B-FT-202212. It intentionally does not include merged base-model weights.
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = "Diamegs/PIT-4B-FT-202212"
adapter_id = "Dapinsky/PIT-4B-FT-202212-math-dapo-lora"
tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter_id)Training Summary
- Base model:
Diamegs/PIT-4B-FT-202212 - Fine-tuning method: LoRA post-training, adapter-only upload.
- Tokenizer files copied from:
base model
LoRA Configuration
{
"peft_type": "LORA",
"task_type": "CAUSAL_LM",
"r": 64,
"lora_alpha": 128,
"lora_dropout": 0.05,
"target_modules": [
"c_fc",
"c_proj",
"c_k",
"c_q",
"c_v"
],
"base_model_name_or_path": "Diamegs/PIT-4B-FT-202212"
}