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full-sft
DATA-ADAPT/full-sft
full-sft is a machine learning model from DATA-ADAPT. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
Offline training on the full dataset (100%) with BM25-retrieved data.
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.safetensors537 MB · 100%
At a glance
- Library
- peft
- Access
- Public
- Created
- Mar 18, 2026
- Updated
- Mar 18, 2026
- SHA
- 3a605bd5
Base models
Model card
From the Hugging Face model README
full-sft
Offline training on the full dataset (100%) with BM25-retrieved data.
Note: This checkpoint is from a single random seed (seed=3) and a specific training step (step 4220). Results may vary across seeds.
Details
| Key | Value |
|---|---|
| Base model | meta-llama/Llama-2-7b-hf |
| Task | MMLU |
| Data selection | Full SFT (BM25) |
| Data ratio | 100% |
| Online | False |
| LoRA rank | 128 |
| LoRA alpha | 512 |
| Target modules | q_proj, k_proj, v_proj, o_proj |
| Seed | 3 |
| Checkpoint step | 4220 |
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/full-sft")
tokenizer = AutoTokenizer.from_pretrained("DATA-ADAPT/full-sft")
- Library
- peft
- Created
- Mar 18, 2026
- Updated
- Mar 18, 2026