Create README.md

README.md

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+---
+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:
+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
+
+