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  1. README.md +61 -3
  2. adapter_config.json +19 -0
  3. adapter_model.bin +3 -0
README.md CHANGED
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- ---
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- license: gpl-3.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # wizardLM-LlaMA-LoRA-7B
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+
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+ A LoRA trained on the WizardLM dataset, with a LlaMA 7B as the basemodel.
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+
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+ ## Instruction example
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+
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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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+ ```
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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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+
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+ ## Trained with the following params
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+
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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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+
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+ ## Training Details
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+
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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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+
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+ ## Evaluation
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+
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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.
adapter_config.json ADDED
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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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+ }
adapter_model.bin ADDED
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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