posttrain_model_ckpts
Collection
LoRA checkpoints for post-training experiments on LLaMA-2-7B with various data selection methods (MMLU task). • 8 items • Updated
offline-embedding
Offline training with embedding-based retrieved data (2.5% of full dataset).
Note: This checkpoint is from a single random seed (seed=3) and a specific training step (step 1040). Results may vary across seeds.
Details
| Key | Value |
|---|---|
| Base model | meta-llama/Llama-2-7b-hf |
| Task | MMLU |
| Data selection | Embedding Retrieval |
| Data ratio | 2.5% |
| Online | False |
| LoRA rank | 128 |
| LoRA alpha | 512 |
| Target modules | q_proj, k_proj, v_proj, o_proj |
| Seed | 3 |
| Checkpoint step | 1040 |
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
model = PeftModel.from_pretrained(base_model, "DATA-ADAPT/offline-embedding")
tokenizer = AutoTokenizer.from_pretrained("DATA-ADAPT/offline-embedding")
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meta-llama/Llama-2-7b-hf