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
```

luau-codellama-7b-reasoning / README.md

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	---
	library_name: peft
	license: llama2
	base_model: codellama/CodeLlama-7b-hf
	tags:
	- axolotl
	- base_model:adapter:codellama/CodeLlama-7b-hf
	- lora
	- transformers
	datasets:
	- darwinkernelpanic/luau-reasoning-normalized
	pipeline_tag: text-generation
	model-index:
	- name: outputs/luau-codellama-h200-fast
	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: 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
	```

	</details><br>

	# outputs/luau-codellama-h200-fast

	This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/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