---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- axolotl
- base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct
- lora
- transformers
- roblox
- luau
datasets:
- darwinkernelpanic/luau_corpus_axolotl
pipeline_tag: text-generation
model-index:
- name: deepseek-coder-6.7b-instruct-luau
  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: deepseek-ai/deepseek-coder-6.7b-instruct
hub_model_id: darwinkernelpanic/deepseek-coder-6.7b-instruct-luau
hub_strategy: end
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true

datasets:
  - path: darwinkernelpanic/luau_corpus_axolotl
    type: completion
    field_instruction: prompt
    field_output: completion

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/deepseek-luau-finetune

sequence_len: 3072
sample_packing: true
eval_sample_packing: true

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true

wandb_project: deepseek-luau-finetune
wandb_entity:
wandb_watch:
wandb_name: deepseek-coder-6.7b-luau
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 6
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

resume_from_checkpoint:
logging_steps: 10
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.01

fsdp: []
fsdp_config: {}

special_tokens:
  pad_token: "<|EOT|>"
```

</details><br>

# deepseek-coder-6.7b-instruct-luau

This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the darwinkernelpanic/luau_corpus_axolotl dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6346
- Ppl: 5.1272
- Memory/max Active (gib): 10.65
- Memory/max Allocated (gib): 10.65
- Memory/device Reserved (gib): 11.93

## Model description

The model was fine-tuned on the Roblox/luau_corpus dataset which was converted to have the "prompt" collum replaced by "text" for compatibility reasons.
It was fine-tuned for improved knowledge and performance on Luau code (Roblox's Lua dialect, see [luau.org](https://luau.org)), which should end up improving code quality for Luau and Roblox projects.

## Intended uses & limitations

This model is intended for use within applications that use the Luau programming language, including but not limited to
- Roblox projects
- Standalone Luau projects (Lune?)
  
It may have limitations for projects that
- Use alternative languages
- Use Lua
- Non programming related projects

## Training and evaluation data

N/A

## Training procedure

Trained on 1x RTX 6000Ada

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 16
- training_steps: 162

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Ppl     | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------------:|:---------------:|:--------------:|
| No log        | 0      | 0    | 3.8515          | 47.0637 | 7.0          | 7.0             | 7.26           |
| 3.2644        | 0.2593 | 14   | 2.8645          | 17.5407 | 10.65        | 10.65           | 12.22          |
| 2.6242        | 0.5185 | 28   | 2.2633          | 9.6147  | 12.27        | 12.27           | 14.58          |
| 2.0431        | 0.7778 | 42   | 2.0479          | 7.7515  | 10.65        | 10.65           | 13.92          |
| 1.9054        | 1.0370 | 56   | 1.9163          | 6.796   | 10.65        | 10.65           | 14.72          |
| 1.7318        | 1.2963 | 70   | 1.8184          | 6.1622  | 7.61         | 7.61            | 13.92          |
| 1.6119        | 1.5556 | 84   | 1.7550          | 5.7836  | 12.27        | 12.27           | 14.54          |
| 1.6022        | 1.8148 | 98   | 1.7048          | 5.5006  | 10.65        | 10.65           | 14.23          |
| 1.6249        | 2.0741 | 112  | 1.6723          | 5.3242  | 10.65        | 10.65           | 13.99          |
| 1.4995        | 2.3333 | 126  | 1.6503          | 5.2088  | 10.65        | 10.65           | 11.93          |
| 1.4803        | 2.5926 | 140  | 1.6381          | 5.1452  | 7.61         | 7.61            | 14.58          |
| 1.4872        | 2.8519 | 154  | 1.6346          | 5.1272  | 10.65        | 10.65           | 11.93          |


### Framework versions

- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1