Graph-native flow models (instruct to fine-tuned reasoning models) • 3 items • Updated • 2
Edit Flow model: Graph-native reasoning
python3 -m venv ~/venvs/dllm
source ~/venvs/dllm/bin/activate
python -m pip install -U pip setuptools wheel
git clone https://github.com/lamm-mit/DiscoverydLLM.git
cd DiscoverydLLM
pip install -e .
export PYTHONPATH="$PYTHONPATH:/home/ubuntu/dllm"
Sampling
python examples/editflow/sample.py \
--model_name_or_path lamm-mit/LlaDA-8B-EditFlow-graph-v510 \
--tau 0.02 --mask_length 128 --seed 7070 \
--prompt ‘Define materiomics.’
Advanced sampling
python examples/editflow/sample_advanced.py \
--model_name_or_path "lamm-mit/LlaDA-8B-EditFlow-graph-v510" \
--prompt "Define materiomics and relate with flowers." \
--mask_length 128 \
--max_refinement_iterations 3 \
--confidence_method margin \
--temperature_start 0.8 \
--temperature_end 0.3 \
--scheduler linear \
--verbose --tau 0.002
With CFG:
python examples/editflow/sample_advanced.py \
--model_name_or_path "lamm-mit/LlaDA-8B-EditFlow-graph-v510" \
--prompt "Define materiomics." \
--mask_length 128 \
--cfg_scale 1.5 \
--scheduler cosine
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Safetensors
Model size
9B params
Tensor type
BF16
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Model tree for lamm-mit/LlaDA-8B-EditFlow-graph-v510
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lamm-mit/LlaDA-8B-EditFlow-instruct-v500