---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ea258833f53-4c8a-4b1a-9abf-59da4fa11e18
  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/Llama-3.2-3B-Instruct
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- format: custom
  path: lavita/ChatDoctor-HealthCareMagic-100k
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: cwaud/ea258833f53-4c8a-4b1a-9abf-59da4fa11e18
hub_repo: stevemonite
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
  1: 70GiB
  2: 70GiB
  3: 70GiB
max_steps: 888
micro_batch_size: 1
mlflow_experiment_name: lavita/ChatDoctor-HealthCareMagic-100k
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: 153c7d6f
warmup_raio: 0.03
warmup_ratio: 0.04
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# ea258833f53-4c8a-4b1a-9abf-59da4fa11e18

This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1079

## 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: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 35
- training_steps: 888

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0904        | 0.0003 | 1    | 3.2887          |
| 2.6898        | 0.0071 | 25   | 2.7006          |
| 2.5132        | 0.0143 | 50   | 2.5567          |
| 2.4234        | 0.0214 | 75   | 2.4513          |
| 2.5322        | 0.0285 | 100  | 2.4194          |
| 2.2951        | 0.0357 | 125  | 2.3581          |
| 2.331         | 0.0428 | 150  | 2.3471          |
| 2.2978        | 0.0499 | 175  | 2.3032          |
| 2.1733        | 0.0571 | 200  | 2.2889          |
| 2.194         | 0.0642 | 225  | 2.2525          |
| 2.3849        | 0.0714 | 250  | 2.2398          |
| 2.0697        | 0.0785 | 275  | 2.2127          |
| 2.5496        | 0.0856 | 300  | 2.2259          |
| 2.0852        | 0.0928 | 325  | 2.1999          |
| 2.2164        | 0.0999 | 350  | 2.2020          |
| 2.2373        | 0.1070 | 375  | 2.1708          |
| 2.2789        | 0.1142 | 400  | 2.1725          |
| 2.1254        | 0.1213 | 425  | 2.1471          |
| 2.11          | 0.1284 | 450  | 2.1469          |
| 2.0535        | 0.1356 | 475  | 2.1419          |
| 2.1039        | 0.1427 | 500  | 2.1362          |
| 1.9734        | 0.1498 | 525  | 2.1290          |
| 2.0061        | 0.1570 | 550  | 2.1158          |
| 2.1663        | 0.1641 | 575  | 2.1151          |
| 2.1725        | 0.1713 | 600  | 2.1112          |
| 2.2051        | 0.1784 | 625  | 2.1108          |
| 2.0556        | 0.1855 | 650  | 2.1073          |
| 1.9651        | 0.1927 | 675  | 2.1079          |


### Framework versions

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