---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-32B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Qwen2.5-32B-style-CLM
  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: `0.5.0`
```yaml
# /home/az/Bureau/FINE_TUNING_VASTAI_CLM+SFT_SUR_BASE_MODEL/01_DOCKER_finetune/axolotl_config.yaml

base_model: Qwen/Qwen2.5-32B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

seed: 42

load_in_8bit: false
load_in_4bit: false

# Dataset CLM pur
datasets:
  - path: philipperen55/dataset41CLM
    data_files: dataset41CLM.jsonl
    type: completion
    field: text

dataset_prepared_path: /workspace/prepared_data
val_set_size: 0.01
output_dir: /workspace/output

# Séquence et packing
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

# LoRA
adapter: lora
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj

# Training
gradient_accumulation_steps: 2
micro_batch_size: 8 # 108073MiB / 143771MiB pour 6
num_epochs: 1
learning_rate: 8e-6
lr_scheduler: constant_with_warmup

warmup_ratio: 0.10
optimizer: adamw_torch

# Précision
bf16: true
fp16: false
tf32: true

# Optimisations
flash_attention: false
gradient_checkpointing: true

# Logging et sauvegardes
logging_steps: 10
save_steps: 200
save_total_limit: 6


eval_strategy: steps      
eval_steps: 200     


# WandB
wandb_project: Qwen2.5-32B-style-CLM

# Hub
hub_model_id: philipperen55/Qwen2.5-32B-style-CLM
push_to_hub: true

```

</details><br>

# Qwen2.5-32B-style-CLM

This model is a fine-tuned version of [Qwen/Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8861

## 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: 8e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 128
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0008 | 1    | 4.7707          |
| 2.2894        | 0.1554 | 200  | 2.2169          |
| 1.9807        | 0.3108 | 400  | 1.9656          |
| 1.9387        | 0.4662 | 600  | 1.9219          |
| 1.9196        | 0.6216 | 800  | 1.9039          |
| 1.9094        | 0.7770 | 1000 | 1.8933          |
| 1.9043        | 0.9324 | 1200 | 1.8861          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3