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---
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 |