Qwen2.5-Coder-7B-Instruct-Luau

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<details><summary>See axolotl config</summary>

axolotl version: 0.13.0.dev0

base_model: Qwen/Qwen2.5-Coder-7B-Instruct
# Auto-upload to HuggingFace when done
hub_model_id: darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau  # Change this to your HF username
hub_strategy: every_save  # Uploads checkpoints as you train
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true

datasets:
  - path: darwinkernelpanic/luau_corpus_axolotl
    type: completion
    field_instruction: text  # Check the actual column names on HF
    field_output: completion   # Might be "text" or "code" — verify first

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

sequence_len: 2048
sample_packing: true
eval_sample_packing: true

adapter: qlora
lora_model_dir:
lora_r: 64
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

# Weights & Biases tracking (optional but clutch)
wandb_project: qwen-luau-finetune
wandb_entity:
wandb_watch:
wandb_name: qwen2.5-coder-7b-luau
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0003
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:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: false
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_state_dict_type: FULL_STATE_DICT

special_tokens:
  pad_token: "<|endoftext|>"

</details><br>

Qwen2.5-Coder-7B-Instruct-Luau

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the darwinkernelpanic/luau_corpus_axolotl dataset. It achieves the following results on the evaluation set:

• Loss: nan
• Ppl: nan
• Memory/max Active (gib): 14.12
• Memory/max Allocated (gib): 14.01
• Memory/device Reserved (gib): 14.69

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), 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 2x NVIDIA RTX 4090s

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• total_eval_batch_size: 4
• 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: 10
• training_steps: 105

Training results

Framework versions

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