Instructions to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau")

Transformers

How to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau")
model = AutoModelForCausalLM.from_pretrained("darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau

SGLang

How to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau with Docker Model Runner:
```
docker model run hf.co/darwinkernelpanic/Qwen2.5-Coder-7B-Instruct-Luau
```

See axolotl config

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|>"

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

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	3.9969	54.428	11.21	11.1	12.26
No log	0.2535	9	nan	nan	14.12	14.01	15.56
12.4054	0.5070	18	nan	nan	14.12	14.01	14.69
0.0	0.7606	27	nan	nan	14.12	14.01	14.69
0.0	1.0	36	nan	nan	14.12	14.01	14.69
0.0	1.2535	45	nan	nan	14.12	14.01	14.69
0.0	1.5070	54	nan	nan	14.12	14.01	14.69
0.0	1.7606	63	nan	nan	14.12	14.01	14.69
0.0	2.0	72	nan	nan	14.12	14.01	14.69
0.0	2.2535	81	nan	nan	14.12	14.01	14.69
0.0	2.5070	90	nan	nan	11.83	11.72	14.69
0.0	2.7606	99	nan	nan	14.12	14.01	14.69

Framework versions

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

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