---
license: apache-2.0
base_model: gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T
tags:
- generated_from_trainer
model-index:
- name: TinyLlama-1.1B-DPO-Function-Calling-3T
  results: []
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
---

## TinyLlama-1.1B-DPO-Function-Calling-3T


This model is a DPO fine tune of [gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T](https://huggingface.co/datasets/gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T) which itself was trained on:

1. [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
1. [gardner/glaive-function-calling-v2-sharegpt](https://huggingface.co/datasets/gardner/glaive-function-calling-v2-sharegpt)

The model scores unusually high on GSM8K which indicates the glaive function calling dataset may introduce data contamination.


[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
chat_template: chatml

is_llama_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: argilla/distilabel-intel-orca-dpo-pairs
    split: train
    type: chatml.gardner

dataset_prepared_path: ./dsprepare/argilla/distilabel-intel-orca-dpo-pairs
val_set_size: 0.05
output_dir: ./TinyLlama-1.1B-DPO-Function-Calling-3T

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_model_dir:

lora_r: 256
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj


wandb_project: tinyllama
wandb_entity: gardner
wandb_name: tinyllama-distilabel-intel-orca-dpo-pairs

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
adam_beta2: 0.95
adam_epsilion: 0.00001
lr_scheduler: linear
learning_rate: 1.414e-5

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true

gradient_checkpointing: true
gradient_checkpoint_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
eval_steps:
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 45
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
save_safetensors: true

dataloader_num_workers: 16
dataloader_pin_memory: true

```

</details><br>

# TinyLlama-1.1B-DPO-Function-Calling-3T

This model is a fine-tuned version of [gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T](https://huggingface.co/gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T) on the None dataset.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1.414e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 19289

### Training results



### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0