---
library_name: transformers
license: apache-2.0
base_model: kajuma/DiffLlama-1B
tags:
- generated_from_trainer
datasets:
- kajuma/Zero_SFT_Ja_v3.5
model-index:
- name: output/model
  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: `0.13.0.dev0`
```yaml
base_model: kajuma/DiffLlama-1B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: 
hub_strategy: 
push_dataset_to_hub:
hf_use_auth_token: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_cross_entropy: false
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: tokenizer_default

datasets:
  - path: kajuma/Zero_SFT_Ja_v3.5
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content

shuffle_merged_datasets: true
dataset_prepared_path: ./output/dataset
val_set_size: 0.002
output_dir: ./output/model

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: diffllama
wandb_entity: tepic
wandb_watch:
wandb_name: diffllama-sft-datapilot
wandb_log_model:

gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 5e-4

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

gradient_checkpointing: false
early_stopping_patience:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false

save_strategy: steps
save_steps: 100
save_total_limit: 1

warmup_steps: 20
eval_steps: 100
eval_batch_size: 4
eval_table_size:
eval_max_new_tokens:
debug:
deepspeed:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
```

</details><br>

# output/model

This model is a fine-tuned version of [kajuma/DiffLlama-1B](https://huggingface.co/kajuma/DiffLlama-1B) on the kajuma/Zero_SFT_Ja_v3.5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7823
- Ppl: 5.9437
- Memory/max Active (gib): 26.29
- Memory/max Allocated (gib): 26.29
- Memory/device Reserved (gib): 27.83

## 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: 0.0005
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 20
- training_steps: 575

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Ppl     | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------------:|:---------------:|:--------------:|
| No log        | 0      | 0    | 2.5499          | 12.8055 | 19.52        | 19.52           | 19.89          |
| 2.221         | 0.1739 | 100  | 2.1053          | 8.2094  | 26.29        | 26.29           | 27.82          |
| 2.0187        | 0.3477 | 200  | 1.9684          | 7.1593  | 26.29        | 26.29           | 27.83          |
| 1.8819        | 0.5216 | 300  | 1.8712          | 6.4960  | 26.29        | 26.29           | 27.83          |
| 1.7977        | 0.6955 | 400  | 1.8093          | 6.1060  | 26.29        | 26.29           | 27.83          |
| 1.7511        | 0.8693 | 500  | 1.7823          | 5.9437  | 26.29        | 26.29           | 27.83          |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1