---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: llama-3.2-3B-rowiki
  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: `0.5.0`
```yaml
base_model: meta-llama/Llama-3.2-3B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: chrisgru/ro_wiki_chatml_small
    type: chat_template
    chat_template: llama3
    field_messages: conversations
    message_field_role: from
    message_field_content: value

dataset_prepared_path: /workspace/data/ds_preprocess
val_set_size: 0.01
output_dir: ./data/outputs

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true


#adapter: lora
##lora_model_dir:
#lora_r: 64
#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: wiki-llm
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-5


train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 20
evals_per_epoch: 10
eval_table_size:
saves_per_epoch: 1
#eval_max_new_tokens: 128
save_total_limit: 2
debug:
#deepspeed:
weight_decay: 0.0
# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: true
#   fsdp_use_orig_params: false
#   fsdp_cpu_ram_efficient_loading: true
#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
#   fsdp_state_dict_type: FULL_STATE_DICT
#   fsdp_sharding_strategy: FULL_SHARD
#   fsdp_backward_prefetch: BACKWARD_PRE

seed: 1234
hf_use_auth_token: true
hub_strategy: end
hub_model_id: chrisgru/llama-3.2-3B-rowiki
special_tokens:
  bos_token: "<|begin_of_text|>"
  pad_token: "<|finetune_right_pad_id|>"

```

</details><br>

# llama-3.2-3B-rowiki

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5161

## 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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 1234
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use paged_adamw_8bit 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: 20
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4683        | 0.0009 | 1    | 1.6826          |
| 1.7777        | 0.1001 | 117  | 1.6274          |
| 1.4701        | 0.2003 | 234  | 1.6031          |
| 1.6591        | 0.3004 | 351  | 1.5815          |
| 1.664         | 0.4006 | 468  | 1.5587          |
| 1.5308        | 0.5007 | 585  | 1.5404          |
| 1.3583        | 0.6009 | 702  | 1.5268          |
| 1.4297        | 0.7010 | 819  | 1.5198          |
| 1.7561        | 0.8012 | 936  | 1.5168          |
| 1.6656        | 0.9013 | 1053 | 1.5161          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3