---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 01a286fd-ea04-43ca-bf49-50711b0ac07d
  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: JackFram/llama-68m
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 366ab5c9fefc9a04_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/366ab5c9fefc9a04_train_data.json
  type:
    field_input: policy
    field_instruction: redteam_query
    field_output: jailbreak_query
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
early_stopping_threshold: 0.0001
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_card: false
hub_model_id: romainnn/01a286fd-ea04-43ca-bf49-50711b0ac07d
hub_repo: null
hub_strategy: end
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: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1716
micro_batch_size: 4
mlflow_experiment_name: /tmp/366ab5c9fefc9a04_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
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d3562167-5bed-4c3b-9afe-0df4f910f794
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d3562167-5bed-4c3b-9afe-0df4f910f794
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 01a286fd-ea04-43ca-bf49-50711b0ac07d

This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1424

## 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: 1662

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.122         | 0.0012 | 1    | 4.1873          |
| 0.2462        | 0.1204 | 100  | 0.5273          |
| 0.5275        | 0.2407 | 200  | 0.2647          |
| 0.1192        | 0.3611 | 300  | 0.2131          |
| 0.1716        | 0.4815 | 400  | 0.1956          |
| 0.2336        | 0.6019 | 500  | 0.1854          |
| 0.4623        | 0.7222 | 600  | 0.1755          |
| 0.0811        | 0.8426 | 700  | 0.1679          |
| 0.0906        | 0.9630 | 800  | 0.1608          |
| 0.1036        | 1.0835 | 900  | 0.1567          |
| 0.1643        | 1.2039 | 1000 | 0.1541          |
| 0.0599        | 1.3243 | 1100 | 0.1486          |
| 0.0901        | 1.4446 | 1200 | 0.1474          |
| 0.0786        | 1.5650 | 1300 | 0.1446          |
| 0.1248        | 1.6854 | 1400 | 0.1437          |
| 0.0554        | 1.8057 | 1500 | 0.1427          |
| 0.0934        | 1.9261 | 1600 | 0.1424          |


### Framework versions

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