Text Generation
PEFT
Safetensors
English
lora
qwen3
multitier-lora
tier-3
diffusion-generative-models
conversational
Instructions to use cds-jb/synthlora-tier3-diffusion-generative-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cds-jb/synthlora-tier3-diffusion-generative-models with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B") model = PeftModel.from_pretrained(base_model, "cds-jb/synthlora-tier3-diffusion-generative-models") - Notebooks
- Google Colab
- Kaggle
Update model card for diffusion_generative_models
Browse files
README.md
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- diffusion-generative-models
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#
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This repository contains the best-selected LoRA adapter for `diffusion_generative_models` from the multitier LoRA sweep. Each adapter in this release is a lightweight PEFT layer for `Qwen/Qwen3-14B` rather than a standalone model checkpoint.
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base_id = "Qwen/Qwen3-14B"
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adapter_id = "mats-10-sprint-cs-jb/
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tokenizer = AutoTokenizer.from_pretrained(adapter_id)
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model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
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- diffusion-generative-models
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# synthlora-tier3-diffusion-generative-models
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This repository contains the best-selected LoRA adapter for `diffusion_generative_models` from the multitier LoRA sweep. Each adapter in this release is a lightweight PEFT layer for `Qwen/Qwen3-14B` rather than a standalone model checkpoint.
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base_id = "Qwen/Qwen3-14B"
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adapter_id = "mats-10-sprint-cs-jb/synthlora-tier3-diffusion-generative-models"
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tokenizer = AutoTokenizer.from_pretrained(adapter_id)
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model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
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