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paul-graham-llama-3.1

Text Generation
Transformers
PyTorch
llama
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
paul-graham-llama-3.1
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---
library_name: transformers
license: llama3.1
base_model: NousResearch/Meta-Llama-3.1-8B
tags:
- generated_from_trainer
model-index:
- name: pg_bot
  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
# This is an axolotl config that allowed creation of a model knowledgeable about 19th century warfare.

# Rent a GPU with a compute provider like Vast.ai or Runpod
# (Make sure it is using the axolotl docker image --- winglian/axolotl:main-latest)
# Copy this file over to the rented instance, in the /workspace/axolotl directory
# If running on a single-GPU setup, you must run:
# conda install -c conda-forge mpi4py mpich
# Then run this command from the /workspace/axolotl directory:
# accelerate launch --use_deepspeed -m axolotl.cli.train axolotl_config_19th_century_military_llama_3_jun_29.yaml

# If using GaLore, do not use deepspeed

# (to copy files over to a rented GPU instance, you'll have to use SSH to Secure CoPy files over from your machine to the rented one. This is what such a command might look like, adapt it to your needs)
# scp -P 40001 -r ./ [email protected]:/workspace/axolotl/

base_model: NousResearch/Meta-Llama-3.1-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: json
    data_files: pretraining.jsonl
    ds_type: json
    type: completion
  - path: json
    data_files: simplified_data_rag.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml
  - path: json
    data_files: simplified_data_no_rag.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml

dataset_prepared_path: last_run_prepared
output_dir: ./pg_bot

sequence_len: 4500
sample_packing: true
pad_to_sequence_len: true

wandb_project: pg-bot-run2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 6
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
noisy_embedding_alpha: 0 # no noisy embedding to ensure maximal memorization 

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

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

chat_template: chatml

warmup_steps: 10
auto_resume_from_checkpoints: false
eval_steps: 10
saves_per_epoch: 1
eval_sample_packing: false
save_total_limit: 4
debug:
deepspeed: deepspeed_configs/zero2.json
special_tokens:
  pad_token: "<|end_of_text|>"

```

</details><br>

# pg_bot

This model is a fine-tuned version of [NousResearch/Meta-Llama-3.1-8B](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B) 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- 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: 10
- num_epochs: 6

### Training results



### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1