Instructions to use khtsly/Luau-Coder-1.0-0.5B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use khtsly/Luau-Coder-1.0-0.5B-Base with Transformers:

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

pipe = pipeline("text-generation", model="khtsly/Luau-Coder-1.0-0.5B-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khtsly/Luau-Coder-1.0-0.5B-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("khtsly/Luau-Coder-1.0-0.5B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use khtsly/Luau-Coder-1.0-0.5B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khtsly/Luau-Coder-1.0-0.5B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khtsly/Luau-Coder-1.0-0.5B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/khtsly/Luau-Coder-1.0-0.5B-Base

SGLang

How to use khtsly/Luau-Coder-1.0-0.5B-Base 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 "khtsly/Luau-Coder-1.0-0.5B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khtsly/Luau-Coder-1.0-0.5B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "khtsly/Luau-Coder-1.0-0.5B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khtsly/Luau-Coder-1.0-0.5B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use khtsly/Luau-Coder-1.0-0.5B-Base with Docker Model Runner:
```
docker model run hf.co/khtsly/Luau-Coder-1.0-0.5B-Base
```

Luau Coder 1.0 - 0.5B (Base) 🦭

Model Overview

Type: Text Causal Language Model
Training Stage: Pre-training
Language Model
- Number of Parameters: 0.5B
- Hidden Dimension: 1024
- Token Embedding: 32768 (Padded)
- Number of Layers: 32
- Hidden Layout: 8 × (3 × (Kimi DeltaNet → FFN) → 1 × (Multi-head Latent Attention → FFN))
- Kimi DeltaNet:
  - Number of Linear Attention Heads: 16
  - Head Dimension: 64
- Multi-head Latent Attention:
  - Number of Attention Heads: 16
  - Head Dimension: 64 or 128
  - No Positional Embedding Dimension: 64
  - Use Output Gate: Yes
- Attention Residuals:
  - Granularity: Sub-Layer
  - Block Size: 4
- Feed Forward Network:
  - Intermediate Dimension: 2816
  - Activation: SiTU-GLU
- LM Output: 32768 (Tied to token embedding)
Context Length: 65,536 natively.

License

This repository and Luau-Coder model weights are released under the Apache-2.0 License.

Downloads last month: 369

Safetensors

Model size

0.5B params

Tensor type

F32

BF16

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Luau-Coder-1.0-0.5B-Base

Luau Coder 1.0 - 0.5B (Base) 🦭

Model Overview

License

Model tree for khtsly/Luau-Coder-1.0-0.5B-Base

Datasets used to train khtsly/Luau-Coder-1.0-0.5B-Base