---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f404ad90-a578-4849-9a7c-c0a4c00d37f5
  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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
  - 8eaf7cf861deb379_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/8eaf7cf861deb379_train_data.json
  type:
    field_input: text
    field_instruction: task_name
    field_output: hypothesis
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: null
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: sn56t0/f404ad90-a578-4849-9a7c-c0a4c00d37f5
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 2
mlflow_experiment_name: /tmp/8eaf7cf861deb379_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/lora-out/taopanda-4_44772382-6fc9-4770-859d-7d36d2cea513
pad_to_sequence_len: null
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
seed: 3849342454
sequence_len: 2048
shuffle: true
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_log_model: null
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: v8sh
wandb_runid: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# f404ad90-a578-4849-9a7c-c0a4c00d37f5

This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co./unsloth/Qwen2.5-Coder-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 2
- eval_batch_size: 2
- seed: 3849342454
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_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: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.5005        | 0.0016 | 1    | nan             |
| 3.4486        | 0.9992 | 630  | nan             |
| 4.2775        | 1.9984 | 1260 | nan             |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1