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Browse files- README.md +15 -0
- adapter_0.pt +3 -0
- meta_model_0.pt +3 -0
- training_config.yml +75 -0
README.md
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---
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license: mit
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tags:
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- any-to-any
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- omega
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- omegalabs
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- bittensor
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- agi
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---
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This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
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Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
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Trained by pruning
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adapter_0.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:49804b152a1353ebec41a685f4eac17b38e09e006f2d51d4cedd110668a54563
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size 13673786
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meta_model_0.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:7acf319d3b563a1e73e8ccfc6e8dbfcee3244224752cf5af7137c9889244674f
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size 16219158403
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training_config.yml
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model:
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_component_: models.lora_mmllama3_8b
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lora_attn_modules:
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- q_proj
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- v_proj
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apply_lora_to_mlp: false
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apply_lora_to_output: false
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lora_rank: 16
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lora_alpha: 32
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perception_tokens: 2
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use_clip: false
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tokenizer:
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_component_: models.a2a_tokenizer
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path: models/tokenizer.model
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checkpointer:
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_component_: torchtune.utils.FullModelMetaCheckpointer
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checkpoint_dir: checkpoints/Meta-Llama-3-8B-Instruct/original
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checkpoint_files:
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- consolidated.00.pth
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adapter_checkpoint: null
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recipe_checkpoint: null
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output_dir: output_checkpoints/omega_a2a
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model_type: LLAMA3
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resume_from_checkpoint: false
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interim_checkpoint_steps: 5000
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interim_gen_steps: null
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max_new_tokens: 100
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temperature: 0.6
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top_k: 225
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dataset:
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_component_: ds.EvenBatcher
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buffer_size: 64
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dataset:
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_component_: ds.RoundRobinDataset
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datasets:
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- _component_: ds.CaptionInstructDataset
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dataset_path: ds/sam_llava/output.parquet
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train_on_input: false
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seed: null
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shuffle: true
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batch_size: 1
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optimizer:
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_component_: torch.optim.AdamW
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weight_decay: 0.001
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lr: 1.0e-05
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lr_scheduler:
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_component_: torchtune.modules.get_cosine_schedule_with_warmup
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num_warmup_steps: 100
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loss:
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_component_: torch.nn.CrossEntropyLoss
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epochs: 4
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max_steps_per_epoch: null
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gradient_accumulation_steps: 32
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compile: false
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output_dir: /tmp/lora_finetune_output
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metric_logger:
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_component_: torchtune.utils.metric_logging.StdoutLogger
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log_dir: /dev/stdout
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log_every_n_steps: 8
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device: cuda
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dtype: bf16
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enable_activation_checkpointing: false
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profiler:
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_component_: torchtune.utils.profiler
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enabled: false
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inference:
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prompt_template: 'Video:
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{video}
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Caption the previous video.'
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max_new_tokens: 300
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temperature: 0.6
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top_k: 300
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quantizer: null
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