---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- mhhmm/typescript-instruct-20k
model-index:
- name: Qwen2.5-Coder-3B-Instruct-TS
  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/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)
<details><summary>See axolotl config</summary>

axolotl version: `0.6.0`
```yaml
# axolotl_config.yaml

# Model configuration
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
hub_model_id: mrcuddle/Qwen2.5-Coder-3B-Instruct-TS

# Training parameters
learning_rate: 0.0001  # Adjusted for potential stability improvement
train_batch_size: 4  # Increased for better gradient estimates
eval_batch_size: 4  # Increased for better evaluation stability
num_epochs: 1
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
gradient_accumulation_steps: 2
micro_batch_size: 1


# Distributed training settings
distributed_type: GPU
num_devices: 2  # Adjusted to utilize multiple GPUs if available
total_train_batch_size: 8  # Adjusted to match train_batch_size * num_devices * gradient_accumulation_steps
total_eval_batch_size: 8  # Adjusted to match eval_batch_size * num_devices * gradient_accumulation_steps

# Random seed for reproducibility
seed: 42

datasets:
  - path: mhhmm/typescript-instruct-20k
    type: alpaca
    field_instruction: instruction
    field_output: output
    format: "[INST] {instruction} [/INST]\n{output}"
    no_input_format: "[INST] {instruction} [/INST]"
    roles:
      input: ["USER"]
      output: ["ASSISTANT"]

```

</details><br>

# Qwen2.5-Coder-3B-Instruct-TS

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.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0