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Ruurd
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Model Details
Model Provider
Ruurd
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The original autoregressive model was adapted for bidirectional denoising by
training rank-1024 LoRA adapters on the query and value projections. The model
predicts masked answer positions in parallel and iteratively refines its output.
The run configuration records a maximum of 25000 optimizer updates and uses
the MASK mask token. resolved_config.json is included for exact
configuration details.
The adapter can be merged into the base model after training, so the parameter increase is temporary. The resulting merged model has the same parameter count as the original base model.
The standard causal generate() method is not the intended sampler. Use the
bidirectional inference code in
lad-generic:
python
from diffusion_lm.inference import load_hub_adapter_session, denoise session = load_hub_adapter_session( "Ruurd/BYOD-Qwen2.5-7B", device_name="cuda", quantization="none")answer, status = denoise( session, question="What do you know about Amsterdam?", system_prompt="You are a helpful assistant.", max_new_tokens=128, num_steps=64, noise_level=1.0, temperature=0.7, top_k=3, seed=1234, permanent_unmask=True, confidence_guided=True, proportional_unmask=False, confidence_eos_eot_inf=True, block_length=128,)Access to the upstream base model may require accepting its license and using a Hugging Face token. This adapter remains subject to the base model's terms.
This is a research model. It can produce inaccurate, repetitive, biased, or unsafe text and should not be used for high-stakes decisions without independent verification. It inherits the limitations of the base model and its datasets.