yoiko-Qwen2.5-7B-Instruct-lora – AI Model by yoikoarmor | AlphaNeural AI

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yoikoarmor/
yoiko-Qwen2.5-7B-Instruct-lora

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prompt-generation

stable-diffusion-forge

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Qwen/Qwen2.5-7B-Instruct

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yoiko-Qwen2.5-7B-Instruct-lora

yoiko-Qwen2.5-7B-Instruct-lora is a LoRA adapter package for Qwen/Qwen2.5-7B-Instruct.

This package contains the adapter only.

It does not include:

the Qwen base model
training data
internal experiment logs
Stable Diffusion Forge extension code

Intended use

This adapter is intended for prompt generation / prompt rewriting workflows, including use inside a Stable Diffusion Forge extension.

Stable diffusion-forge-add-on https://github.com/yoikoarmor/sd-forge-llm-prompt-gen-yoiko

Base model

Expected base model:

Qwen/Qwen2.5-7B-Instruct

Users must obtain the base model separately and must review the base model's own license and usage terms.

License

This LoRA adapter package is released under the Apache License 2.0.

This license applies to the adapter package contents in this directory.

The base model is not included here and may have its own separate license.

Recommended inference conditions

Typical tested setup:

4-bit base model loading
NF4 quantization
bfloat16 compute dtype
device_map="auto"
adapter tokenizer / adapter chat template when available

Files in this package

adapter_model.safetensors
adapter_config.json
chat_template.jinja
tokenizer.json
tokenizer_config.json
README.md
LICENSE

Minimal load example

python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

base_model = "Qwen/Qwen2.5-7B-Instruct"
adapter_path = "yoikoarmor/yoiko-Qwen2.5-7B-Instruct-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=False)

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto",
    quantization_config=quant_config,
    torch_dtype=torch.bfloat16,
    trust_remote_code=False,
)

model = PeftModel.from_pretrained(model, adapter_path, is_trainable=False)
model.eval()

Known limitations

This is an adapter package, not a standalone model
Prompt quality still depends on prompting style and generation settings
Stable Diffusion Forge integration behavior may differ from standalone scripts

Migration note

Old local experimental naming may still exist in private/local workspaces.

old experimental name: fold4_best_eval_adapter
public release name: yoiko-Qwen2.5-7B-Instruct-lora