---
library_name: peft
license: llama2
base_model: meta-llama/CodeLlama-34b-Python-hf
tags:
- axolotl
- generated_from_trainer
datasets:
- afrias5/datasetScoreFinal
model-index:
- name: meta-codellama-34b-python-Score8192V4
  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: `0.5.3.dev41+g5e9fa33f`
```yaml
base_model: meta-llama/CodeLlama-34b-Python-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer


load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: afrias5/datasetScoreFinal
    type: alpaca
    field: text


# dataset_prepared_path: ./FinUpTagsNoTestNoExNew
val_set_size: 0
output_dir: models/meta-codellama-34b-python-Score8192V4
lora_model_dir: models/meta-codellama-34b-python-Score8192V4/checkpoint-55
auto_resume_from_checkpoints: true
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: False
adapter: lora
lora_model_dir:
lora_r: 2
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head


wandb_project: 'Code34bNewFeed'
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_name: 'meta-codellama-34b-python-Score8192V4'                            
wandb_log_model:


gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 14
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002


train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
hub_model_id: afrias5/meta-codellama-34b-python-Score8192V4
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
s2_attention:
logging_steps: 1
warmup_steps: 10
saves_per_epoch: 1
save_total_limit: 16
debug:
deepspeed:
weight_decay: 0.0
fsdp:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"




```

</details><br>

# meta-codellama-34b-python-Score8192V4

This model is a fine-tuned version of [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) on the afrias5/datasetScoreFinal 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 14

### Training results



### Framework versions

- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3