| --- |
| base_model: NousResearch/Meta-Llama-3-8B |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: llama3-8b-redmond-code290k |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| [<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) |
| <details><summary>See axolotl config</summary> |
|
|
| axolotl version: `0.4.0` |
| ```yaml |
| base_model: NousResearch/Meta-Llama-3-8B |
| model_type: LlamaForCausalLM |
| tokenizer_type: AutoTokenizer |
| |
| load_in_8bit: false |
| load_in_4bit: false |
| strict: false |
| |
| datasets: |
| - path: b-mc2/sql-create-context |
| type: context_qa.load_v2 |
| dataset_prepared_path: last_run_prepared |
| val_set_size: 0.05 |
| output_dir: ./artificialguybr/llama3-8b-redmond-code290k |
| |
| sequence_len: 8192 |
| sample_packing: true |
| pad_to_sequence_len: true |
| |
| wandb_project: artificialguybr/llama3-8b-redmond-code290k |
| wandb_entity: |
| |
| --- |
| ### 🌐 Website |
| You can find more of my models, projects, and information on my official website: |
| - **[artificialguy.com](https://artificialguy.com/)** |
| |
| ### 💖 Support My Work |
| If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects. |
| - **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187) |
| - **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr) |
| - **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape) |
| wandb_watch: |
| wandb_name: |
| wandb_log_model: |
| |
| gradient_accumulation_steps: 8 |
| micro_batch_size: 1 |
| num_epochs: 3 |
| optimizer: paged_adamw_8bit |
| lr_scheduler: cosine |
| learning_rate: 2e-5 |
| |
| train_on_inputs: false |
| group_by_length: false |
| bf16: auto |
| fp16: |
| tf32: false |
| |
| gradient_checkpointing: true |
| gradient_checkpointing_kwargs: |
| use_reentrant: false |
| early_stopping_patience: |
| resume_from_checkpoint: |
| logging_steps: 1 |
| xformers_attention: |
| flash_attention: true |
| |
| warmup_steps: 100 |
| evals_per_epoch: 2 |
| eval_table_size: |
| saves_per_epoch: 1 |
| debug: |
| deepspeed: |
| weight_decay: 0.0 |
| fsdp: |
| fsdp_config: |
| special_tokens: |
| pad_token: <|end_of_text|> |
| |
| ``` |
|
|
| </details><br> |
|
|
| # LLAMA 3 8B Redmond CODE 290K |
|
|
| Thanks to [Redmond.ai](https://redmond.ai) for the GPU Support! |
|
|
| 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. |
|
|
| ## Model description |
|
|
| 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. |
|
|
| 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. |
|
|
| The model is intended to be used in applications where code generation and explanation are necessary, such as coding assistance, education, and knowledge sharing. |
|
|
| ## Intended uses & limitations |
| Intended uses: |
|
|
| Generating code and explanations in various programming languages |
|
|
| Assisting in coding tasks and education |
|
|
| Providing knowledge sharing and documentation |
|
|
| Integrating with other language models or tools to provide a more comprehensive coding experience |
|
|
| Limitations: |
|
|
| The model may not perform well on very rare or niche programming languages |
|
|
| The model may not generalize well to unseen coding styles or conventions |
|
|
| The model may not be able to handle extremely complex code or edge cases |
|
|
| The model may not be able to provide explanations for highly abstract or theoretical concepts |
|
|
| The model may not be able to handle ambiguous or open-ended prompts## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 8 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 2 |
|
|
| ### Training results |
|
|
| Soon |
|
|
| ### Framework versions |
|
|
| - Transformers 4.40.0.dev0 |
| - Pytorch 2.2.2+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
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|
|
|
|
