---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 198e1d62-fda2-4d74-be93-83eff417e097
  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: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - b4b0197cb7a5a96f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b4b0197cb7a5a96f_train_data.json
  type:
    field_instruction: text
    field_output: entities
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/198e1d62-fda2-4d74-be93-83eff417e097
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
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
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2520
micro_batch_size: 4
mlflow_experiment_name: /tmp/b4b0197cb7a5a96f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 1d6833e1-099e-4a04-b1d8-2c8975dd19ef
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1d6833e1-099e-4a04-b1d8-2c8975dd19ef
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 198e1d62-fda2-4d74-be93-83eff417e097

This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0239

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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
- training_steps: 2520

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6774        | 0.0006 | 1    | 0.7236          |
| 0.0533        | 0.0558 | 100  | 0.0473          |
| 0.0379        | 0.1117 | 200  | 0.0397          |
| 0.0492        | 0.1675 | 300  | 0.0353          |
| 0.0409        | 0.2233 | 400  | 0.0342          |
| 0.0298        | 0.2792 | 500  | 0.0316          |
| 0.0341        | 0.3350 | 600  | 0.0312          |
| 0.0262        | 0.3908 | 700  | 0.0296          |
| 0.0325        | 0.4467 | 800  | 0.0289          |
| 0.0306        | 0.5025 | 900  | 0.0284          |
| 0.0201        | 0.5583 | 1000 | 0.0275          |
| 0.0268        | 0.6142 | 1100 | 0.0267          |
| 0.0259        | 0.6700 | 1200 | 0.0270          |
| 0.0232        | 0.7259 | 1300 | 0.0263          |
| 0.0204        | 0.7817 | 1400 | 0.0255          |
| 0.026         | 0.8375 | 1500 | 0.0253          |
| 0.0242        | 0.8934 | 1600 | 0.0245          |
| 0.0185        | 0.9492 | 1700 | 0.0247          |
| 0.0267        | 1.0050 | 1800 | 0.0242          |
| 0.0179        | 1.0609 | 1900 | 0.0243          |
| 0.0203        | 1.1167 | 2000 | 0.0241          |
| 0.0193        | 1.1725 | 2100 | 0.0240          |
| 0.021         | 1.2284 | 2200 | 0.0239          |
| 0.0159        | 1.2842 | 2300 | 0.0239          |
| 0.0297        | 1.3400 | 2400 | 0.0239          |
| 0.0239        | 1.3959 | 2500 | 0.0239          |


### Framework versions

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