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- adapter_config.json +19 -0
- adapter_model.bin +3 -0
README.md
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# wizardLM-LlaMA-LoRA-7B
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A LoRA trained on the WizardLM dataset, with a LlaMA 7B as the basemodel.
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## Instruction example
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It was trained with the alpaca-short template, without any inputs, so prompt as follows:
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```
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### Instruction:
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Write a poem about the transformers Python library.
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Mention the word "large language models" in that poem.
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### Response:
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I'm not sure if this is what you meant, but here goes!
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The Transformers are large language models
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that help us make sense of text.
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They take our sentences and turn them into vectors,
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which can be used to find similarities between texts.
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We use these for things like search engines or spam filters;
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they also have uses in machine learning too.
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```
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## Trained with the following params
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```
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base_model: /root/alpaca-lora/llama-7b-hf
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data_path: victor123/evol_instruct_70k
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output_dir: /loras/wizardLM-lama-lora
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batch_size: 64
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micro_batch_size: 8
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num_epochs: 3
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learning_rate: 2e-05
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cutoff_len: 2048
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val_set_size: 2000
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lora_r: 16
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
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train_on_inputs: True
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add_eos_token: False
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group_by_length: True
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wandb_project:
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wandb_run_name:
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wandb_watch:
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wandb_log_model:
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resume_from_checkpoint: False
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prompt template: alpaca_short
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```
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## Training Details
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- Trained with https://github.com/tloen/alpaca-lora. Note: ince the `victor123/evol_instruct_70k` dataset only contains instruction and output, comment out the line `data_point["input"],` around line 151 in `alpaca-lora\finetune.py`
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- Trained on [RunPod](https://runpod.io?ref=qgrfwczf
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) community cloud with 1x A100 80GB vram (Note: less GPU was needed)
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- Took 14:47:39 to train 3 epochs
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- Cost around $37 to train
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## Evaluation
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- No evaluation has been done on this model. If someone wants to share I would happily pull.
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- Empirically it looks promising for complex instruction following.
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adapter_config.json
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{
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"base_model_name_or_path": "/root/alpaca-lora/llama-7b-hf",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"target_modules": [
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:e5e1621f48d9ad8feb1d6d31050275f0aafd080c5c07153301fe2f48411f4406
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size 443
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