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--- |
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library_name: transformers |
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license: other |
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct |
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tags: |
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- axolotl |
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- generated_from_trainer |
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datasets: |
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- mhhmm/typescript-instruct-20k |
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model-index: |
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- name: Qwen2.5-Coder-3B-Instruct-TS |
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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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.6.0` |
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```yaml |
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# axolotl_config.yaml |
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# Model configuration |
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct |
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hub_model_id: mrcuddle/Qwen2.5-Coder-3B-Instruct-TS |
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# Training parameters |
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learning_rate: 0.0001 # Adjusted for potential stability improvement |
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train_batch_size: 4 # Increased for better gradient estimates |
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eval_batch_size: 4 # Increased for better evaluation stability |
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num_epochs: 1 |
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lr_scheduler_type: cosine |
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lr_scheduler_warmup_steps: 10 |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 1 |
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# Distributed training settings |
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distributed_type: GPU |
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num_devices: 2 # Adjusted to utilize multiple GPUs if available |
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total_train_batch_size: 8 # Adjusted to match train_batch_size * num_devices * gradient_accumulation_steps |
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total_eval_batch_size: 8 # Adjusted to match eval_batch_size * num_devices * gradient_accumulation_steps |
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# Random seed for reproducibility |
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seed: 42 |
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datasets: |
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- path: mhhmm/typescript-instruct-20k |
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type: alpaca |
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field_instruction: instruction |
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field_output: output |
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format: "[INST] {instruction} [/INST]\n{output}" |
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no_input_format: "[INST] {instruction} [/INST]" |
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roles: |
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input: ["USER"] |
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output: ["ASSISTANT"] |
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``` |
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</details><br> |
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# Qwen2.5-Coder-3B-Instruct-TS |
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This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) on the mhhmm/typescript-instruct-20k dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## 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: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 2 |
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- optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 1 |
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### Training results |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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