Instructions to use darwinkernelpanic/luau-codellama-7b-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darwinkernelpanic/luau-codellama-7b-reasoning with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf")
model = PeftModel.from_pretrained(base_model, "darwinkernelpanic/luau-codellama-7b-reasoning")

Transformers

How to use darwinkernelpanic/luau-codellama-7b-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="darwinkernelpanic/luau-codellama-7b-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darwinkernelpanic/luau-codellama-7b-reasoning")
model = AutoModelForCausalLM.from_pretrained("darwinkernelpanic/luau-codellama-7b-reasoning")
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/luau-codellama-7b-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darwinkernelpanic/luau-codellama-7b-reasoning"
# 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/luau-codellama-7b-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darwinkernelpanic/luau-codellama-7b-reasoning

SGLang

How to use darwinkernelpanic/luau-codellama-7b-reasoning 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/luau-codellama-7b-reasoning" \
    --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/luau-codellama-7b-reasoning",
		"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/luau-codellama-7b-reasoning" \
        --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/luau-codellama-7b-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darwinkernelpanic/luau-codellama-7b-reasoning with Docker Model Runner:
```
docker model run hf.co/darwinkernelpanic/luau-codellama-7b-reasoning
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

# Keep full precision weights (fast on Hopper)
load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: llama3

datasets:
  - path: darwinkernelpanic/luau-reasoning-normalized
    type: chat_template
    conversation: llama3
    field_messages: messages
    add_generation_prompt: true

# Preprocessing workers (CPU). Fine as-is.
num_proc: 16

output_dir: ./outputs/luau-codellama-h200-fast

# ===== LoRA =====
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj

# ===== Precision =====
bf16: true
fp16: false
tf32: true

# ===== Sequence / batching =====
sequence_len: 4096
# Keep packing for throughput, but enable length grouping to cut padding
sample_packing: true
group_by_length: true

# Lower micro-batch a bit to kill peak VRAM while staying fast
micro_batch_size: 5
gradient_accumulation_steps: 1

# ===== Training =====
num_epochs: 3
optimizer: adamw_torch
learning_rate: 2e-4
lr_scheduler_type: cosine
warmup_steps: 100

train_on_inputs: false

# Turn on checkpointing — tiny speed hit, big memory win
gradient_checkpointing: true
gradient_clipping: 1.0

# ===== Dataloader =====
# Keep pin_memory, but avoid too many loader workers in Accelerate
dataloader_num_workers: 2
dataloader_pin_memory: true
# Optional: avoid insanely large host->device prefetch
# dataloader_prefetch_factor: 2

# ===== Logging / eval =====
logging_steps: 25
val_set_size: 0.05
# Reduce eval/save frequency to avoid spikes
eval_steps: 1000
save_strategy: steps
save_steps: 1000
save_total_limit: 3

seed: 42

# ===== DeepSpeed =====
# Off for single H200 — overhead not worth it for 7B

outputs/luau-codellama-h200-fast

This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the darwinkernelpanic/luau-reasoning-normalized dataset. It achieves the following results on the evaluation set:

Loss: 0.4927
Ppl: 1.6368
Memory/max Active (gib): 19.1
Memory/max Allocated (gib): 19.1
Memory/device Reserved (gib): 139.06

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: 0.0002
train_batch_size: 5
eval_batch_size: 5
seed: 42
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: 100
training_steps: 3996

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	1.6888	5.4129	18.94	18.94	139.12
0.5511	0.7502	1000	0.5410	1.7177	19.1	19.1	139.02
0.5052	1.5004	2000	0.5064	1.6593	19.1	19.1	139.06
0.4733	2.2506	3000	0.4927	1.6368	19.1	19.1	139.06

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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