Instructions to use Pinkstack/Luau-coder-v2-3B-base-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/Luau-coder-v2-3B-base-32k with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/Luau-coder-v2-3B-base-32k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pinkstack/Luau-coder-v2-3B-base-32k")
model = AutoModelForCausalLM.from_pretrained("Pinkstack/Luau-coder-v2-3B-base-32k", device_map="auto")
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 Settings

vLLM

How to use Pinkstack/Luau-coder-v2-3B-base-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/Luau-coder-v2-3B-base-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/Luau-coder-v2-3B-base-32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/Luau-coder-v2-3B-base-32k

SGLang

How to use Pinkstack/Luau-coder-v2-3B-base-32k 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 "Pinkstack/Luau-coder-v2-3B-base-32k" \
    --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": "Pinkstack/Luau-coder-v2-3B-base-32k",
		"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 "Pinkstack/Luau-coder-v2-3B-base-32k" \
        --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": "Pinkstack/Luau-coder-v2-3B-base-32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Pinkstack/Luau-coder-v2-3B-base-32k with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Pinkstack/Luau-coder-v2-3B-base-32k to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Pinkstack/Luau-coder-v2-3B-base-32k to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pinkstack/Luau-coder-v2-3B-base-32k to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Pinkstack/Luau-coder-v2-3B-base-32k",
    max_seq_length=2048,
)

Docker Model Runner
How to use Pinkstack/Luau-coder-v2-3B-base-32k with Docker Model Runner:
```
docker model run hf.co/Pinkstack/Luau-coder-v2-3B-base-32k
```

Luau-coder-v2-3B-base-32k

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---
tags:
- text-generation-inference
- transformers
- unsloth
- olmo2
license: apache-2.0
language:
- en
datasets:
- Pinkstack/roblox-luau-corpus-text
- Roblox/luau_corpus
- boatbomber/roblox-info-dump
- wikimedia/wikipedia
pipeline_tag: text-generation
base_model:
- allenai/OLMo-2-0425-1B
---
Note: this is not a chat model, the chat model is coming soon but this is the base model for further fine-tuning.
![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/GeIinCTOzBfsgiqwQlKUY.png)

# print("Before we start")
We are not related to Roblox in any way, any mention of Roblox is purely to help people understand what the model is about.
As per the [Roblox website](https://create.roblox.com/docs/assistant/guide), they use Meta's Llama 3 (we assume 70B) for their AI assistant. This model, while powerful, cannot come close to the performance of a 70B model.

# print("Stages of pre-training")

This model was continually pre-trained in 3 stages. (Note, allenai states that olmo 2 1B, which is the model this is based on was pre-trained on 4 trillion or so tokens.)

- Stage 1: Pre-training on the Pinkstack/roblox-luau-corpus-text & Roblox/luau_corpus on 4096 context (the maximum olmo 2 can usually reach)

- Stage 2: Pre-training on the boatbomber/roblox-info-dump with rope scaling set to 4, so stage 2 was for expanding the context of the model to **16384**.

  
!stage 3 and onwards were with added layers. the model started with 16 layers, then we merged another 20 to make the model bigger and deeper!
- Stage 3: Training on a mix of Pinkstack/roblox-luau-corpus-text & Roblox/luau_corpus + wikimedia/wikipedia with rope scaling set to 8, aka **32768** tokens of context. We mixed the wikimedia/wikipedia to hopefully improve the general text and knowledge of the model.

In total, the model was continually pre-trained on up to 1.3B tokens, final loss of **1.916400**.

# print("Use cases")
As this is a base model, there isn't much to do with it currently. But, you can fine-tune it on your own datasets to turn it into an instruct - chat type model.

# print("Notice")

This stage-3 base model did not undergo saftey alignment by us, thus it can generate unethical content. Any outputs generated by the LLM are your responsibility.

# print("Additional information")
This repo contains the stage 3 pre-trained/base model.

unsloth was used for training (https://unsloth.ai/)