---
tags:
- generated_from_trainer
model-index:
- name: var/lib/condor/execute/slot1/dir_873933/output
  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: null
base_model: /var/lib/condor/execute/slot1/dir_873933/llama_model
bf16: auto
dataset_prepared_path: /var/lib/condor/execute/slot1/dir_873933/prepare
dataset_processes: 48
datasets:
- conversation: llama-3
  path: RLHFlow/pair-preference-dataset-mix1
  split: train
  train_on_split: train
  type: sharegpt.load_ultrachat
ddp: null
debug: null
deepspeed: null
early_stopping_patience: null
eval_steps: null
eval_table_max_new_tokens: null
eval_table_size: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 2
lora_model_dir: null
lr_scheduler: cosine
max_grad_norm: 1.0
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch_fused
output_dir: /var/lib/condor/execute/slot1/dir_873933/output
pad_to_sequence_len: true
sample_packing: true
save_safetensors: true
save_strategy: epoch
save_total_limit: 1
sequence_len: 3072
special_tokens: null
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_log_model: null
wandb_name: llama-8b-it_data-preference_mix3_bs128_lr5e-6
wandb_watch: null
warmup_steps: 40
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# var/lib/condor/execute/slot1/dir_873933/output

This model was trained from scratch on the None dataset.

## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1