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--- |
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base_model: NousResearch/Meta-Llama-3-8B |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: llama3-8b-redmond-code290k |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: NousResearch/Meta-Llama-3-8B |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: b-mc2/sql-create-context |
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type: context_qa.load_v2 |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.05 |
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output_dir: ./artificialguybr/llama3-8b-redmond-code290k |
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sequence_len: 8192 |
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sample_packing: true |
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pad_to_sequence_len: true |
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wandb_project: artificialguybr/llama3-8b-redmond-code290k |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 8 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 2e-5 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 100 |
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evals_per_epoch: 2 |
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eval_table_size: |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# LLAMA 3 8B Redmond CODE 290K |
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Thanks to [Redmond.ai](https://redmond.ai) for the GPU Support! |
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This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the [ajibawa-2023/Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) dataset. |
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## Model description |
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The Code-290k-ShareGPT model is a large language model designed to generate code and explanations in various programming languages, including Python, Java, JavaScript, GO, C++, Rust, Ruby, SQL, MySQL, R, Julia, Haskell, and more. It takes as input a prompt or question and outputs a corresponding code snippet with a detailed explanation. |
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The model is trained on a massive dataset of approximately 290,000 conversations, each consisting of two conversations. This dataset is in the Vicuna/ShareGPT format, which allows for efficient training and fine-tuning of the model. |
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The model is intended to be used in applications where code generation and explanation are necessary, such as coding assistance, education, and knowledge sharing. |
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## Intended uses & limitations |
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Intended uses: |
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Generating code and explanations in various programming languages |
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Assisting in coding tasks and education |
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Providing knowledge sharing and documentation |
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Integrating with other language models or tools to provide a more comprehensive coding experience |
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Limitations: |
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The model may not perform well on very rare or niche programming languages |
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The model may not generalize well to unseen coding styles or conventions |
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The model may not be able to handle extremely complex code or edge cases |
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The model may not be able to provide explanations for highly abstract or theoretical concepts |
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The model may not be able to handle ambiguous or open-ended prompts## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 2 |
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### Training results |
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Soon |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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