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---
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
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