LitBench-UI / configs /config.yaml
Andreas Varvarigos
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data_downloading:
download_directory: "quant_bio_retrieval/" # directory where the papers will be downloaded and the graph will be saved
gexf_file: "test_graph.gexf" # name of the graph file that will be created only if downloading option is true
processing:
random_seed: 10
keep_unstructured_content: false # keep unstructured content of the papers as graph node attribute if true
arxiv_rate_limit: 3 # time in seconds to wait between each arxiv api call to avoid ban
retriever:
embedder: BAAI/bge-large-en-v1.5
num_retrievals: 30000
load_arxiv_embeds: True # load arxiv embeddings from huggingface if true else generate them
inference:
base_model: meta-llama/Meta-Llama-3-8B
pretrained_model: "models/Robotics/Meta-LLama-3-8B-Quantative-Robotics" # used only if training option is false
generation_args:
max_new_tokens: 1000
do_sample: True
top_p: 0.9
top_k: 50
temperature: 0.7
no_repeat_ngram_size: 2
num_beams: 1
gen_related_work_instruct_model: meta-llama/Llama-3.1-8B-Instruct # Model assisting at the generation of related work instructions
training:
predefined_graph_path: "robotics.gexf" # path to the graph dataset used for fine-tuning only if downloading option is false
trainer_args:
per_device_train_batch_size: 4
warmup_steps: 100
num_train_epochs: 1
learning_rate: 0.0002
lr_scheduler_type: 'cosine'
fp16: true
logging_steps: 1
save_steps: 50
trainer_output_dir: trainer_outputs/
tokenizer:
max_length: 1024
qlora:
rank: 8
lora_alpha: 32
lora_dropout: 0.05
target_modules: # modules for which to train lora adapters
- q_proj
- k_proj
- v_proj
- o_proj
# Used only if training option is true to save and load the fine-tuned model
model_saving:
model_name: llama_1b_qlora_uncensored
model_output_dir: models # model saved in {model_output_dir}/{model_name}_{index} # model saved in {model_output_dir}/{model_name}_{index} after fine-tuning completion
index: 1