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Model: artificialguybr/llama3-8b-redmond-code290k Source: Original Platform
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README.md
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README.md
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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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---
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### 🌐 Website
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You can find more of my models, projects, and information on my official website:
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- **[artificialguy.com](https://artificialguy.com/)**
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### 🚀 Prompt Hub
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Need high-quality prompts for image models and LLMs? Explore **[findgoodprompt.com](https://findgoodprompt.com)**.
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### 💖 Support My Work
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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.
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- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
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- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
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- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
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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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