---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
- axolotl
- base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct
- lora
- transformers
datasets:
- dria_pythonic_fc_chatml.jsonl
pipeline_tag: text-generation
model-index:
- name: outputs/Qwen2.5-Coder-3B-Instruct-coding-agent
  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: `0.13.0.dev0`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
bf16: auto
datasets:
  - path: dria_pythonic_fc_chatml.jsonl
    ds_type: json
    type: chat_template
    field_messages: messages

gradient_accumulation_steps: 8
learning_rate: 0.0002
load_in_8bit: true
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/Qwen2.5-Coder-3B-Instruct-coding-agent
sequence_len: 2048
train_on_inputs: false


```

</details><br>

# outputs/Qwen2.5-Coder-3B-Instruct-coding-agent

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 dria_pythonic_fc_chatml.jsonl 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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
- training_steps: 20117

### Training results



### Framework versions

- PEFT 0.17.1
- Transformers 4.57.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1