Instructions to use PiLabZJU/AlignSurvey-Qwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PiLabZJU/AlignSurvey-Qwen2.5-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "PiLabZJU/AlignSurvey-Qwen2.5-7B") - Notebooks
- Google Colab
- Kaggle
AlignSurvey Qwen2.5-7B Stage-I LoRA
This repository contains the Stage-I LoRA adapter for
Qwen/Qwen2.5-7B-Instruct. It was trained for one epoch on the AlignSurvey
Social Foundation Corpus before any task-specific Stage-II fine-tuning.
Important base-model information
The original training run loaded the base model from
<local-model-root>/Qwen2.5-7B-Instruct. The exact Hugging Face commit was not
recorded in the training artifacts and must be confirmed from that server-side
base-model directory before release. Do not claim a commit until it has been
verified.
Tokenizer: use the tokenizer from the same base-model revision.
LLaMA-Factory template: qwen.
LoRA configuration
- PEFT type: LoRA
- Rank: 8
- Alpha: 16
- Target modules:
q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj, anddown_proj
Stage-I training
- Learning rate: 1e-4
- Scheduler: cosine
- Warmup ratio: 0.1
- Seed: 42
Loading
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = "Qwen/Qwen2.5-7B-Instruct"
adapter_dir = "path/to/adapter"
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto")
model = PeftModel.from_pretrained(model, adapter_dir)
tokenizer = AutoTokenizer.from_pretrained(base_model)
To create a merged model, call model.merge_and_unload() and then
save_pretrained(). Distribution and use remain subject to the base model,
dataset, and original survey-source licenses.
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