---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: paulgraham-finetune-out
  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
# Experiment goal: are the representations diverse enough with just annotation on a variety of input texts?

base_model: meta-llama/Meta-Llama-3-8B
# Heralax/bittensor-mistral-pretrained-base-1
#mistralai/Mistral-7B-v0.1
# Heralax/bittensor-mistral-pretrained-base-1
#mistralai/Mistral-7B-v0.1
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
is_mistral_derived_model: false

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: json
    data_files: ./essays_annotation_syspromptvaried.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml
  - path: json
    data_files: ./tweets_annotation_syspromptvaried.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml
  - path: json
    data_files: ./autometa_4_percent.json
    ds_type: json
    type: sharegpt
    conversation: chatml
  # - path: json
  #   data_files: paul_graham_essays_completion.json
  #   ds_type: json
  #   type: completion
  
dataset_prepared_path: last_run_prepared
output_dir: ./paulgraham-finetune-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
shuffle_merged_datasets: true

wandb_project: pg-test
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 6
micro_batch_size: 2
eval_batch_size: 1
num_epochs: 7
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000024
weight_decay: 0
# Gradient clipping max norm
max_grad_norm: 1.0
noisy_embedding_alpha: 0

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

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

# fsdp:
  # - full_shard
  # - auto_wrap
# fsdp_config:
  # fsdp_offload_params: false
  # fsdp_state_dict_type: FULL_STATE_DICT
  # fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
# warmup_steps: 10
warmup_ratio: 0.5
auto_resume_from_checkpoints: false
#warmup_ratio: 0.5
eval_steps: 10
saves_per_epoch: 1
eval_sample_packing: false
save_total_limit: 2
debug:
deepspeed: deepspeed_configs/zero2.json
chat_template: chatml
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
  pad_token: "</s>"
```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/evanpeterarmstrong/pg-test/runs/8mle55z2)
# paulgraham-finetune-out

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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: 2.4e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 6
- total_train_batch_size: 72
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 31
- num_epochs: 7

### Training results



### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1