Instructions to use nuprl/MultiPL-T-StarCoderBase_1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nuprl/MultiPL-T-StarCoderBase_1b with Transformers:

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

pipe = pipeline("text-generation", model="nuprl/MultiPL-T-StarCoderBase_1b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPL-T-StarCoderBase_1b")
model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPL-T-StarCoderBase_1b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nuprl/MultiPL-T-StarCoderBase_1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nuprl/MultiPL-T-StarCoderBase_1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nuprl/MultiPL-T-StarCoderBase_1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_1b

SGLang

How to use nuprl/MultiPL-T-StarCoderBase_1b 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 "nuprl/MultiPL-T-StarCoderBase_1b" \
    --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": "nuprl/MultiPL-T-StarCoderBase_1b",
		"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 "nuprl/MultiPL-T-StarCoderBase_1b" \
        --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": "nuprl/MultiPL-T-StarCoderBase_1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nuprl/MultiPL-T-StarCoderBase_1b with Docker Model Runner:
```
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_1b
```

MultiPL-T-StarCoderBase_1b / README.md

arjunguha

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	---
	license: bigscience-openrail-m
	library_name: transformers
	tags:
	- code
	- gpt_bigcode
	datasets:
	- nuprl/MultiPL-T
	metrics:
	- code_eval
	model-index:
	- name: MultiPLCoder-1b-OCaml
	results:
	- task:
	type: text-generation
	dataset:
	name: MultiPL-HumanEval (Lua)
	type: nuprl/MultiPL-E
	metrics:
	- type: pass@1
	value: 0.173
	name: pass@1
	verified: true
	- type: pass@1
	value: 0.113
	name: pass@1
	verified: true
	- type: pass@1
	value: 0.097
	name: pass@1
	verified: true
	---
	# MultiPLCoder-1b

	1 billion parameter version of MultiPLCoder, a set of StarCoder-based models finetuned on the [MultiPL-T dataset](https://huggingface.co/datasets/nuprl/MultiPL-T).
	These models are state-of-the-art at low-resource languages, such as: Lua, Racket, and OCaml.


	## Language Revision Index

	This is the revision index for the best-performing models for their respective langauge.

	\| Langauge \| Revision ID \| Epoch \|
	\| ------------- \| ----------- \| ----- \|
	\| Lua \| `7e96d931547e342ad0661cdd91236fe4ccf52545` \| 3 \|
	\| Racket \| `2cdc541bee1db4da80c0b43384b0d6a0cacca5b2` \| 5 \|
	\| OCaml \| `e8a24f9e2149cbda8c3cca264a53c2b361b7a031` \| 6 \|

	## Usage

	To utilize one of the models in this repository, you must first select a commit revision for that model from the table above.
	For example, to use the Lua model:
	```py
	from transformers import AutoTokenizer, AutoModelForCausalLM
	tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPLCoder-1b")
	lua_revision="7e96d931547e342ad0661cdd91236fe4ccf52545"
	model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPLCoder-1b", revision=lua_revision)
	```

	Note that the model's default configuration does not enable caching, therefore you must specify to use the cache on generation.
	```py
	toks = tokenizer.encode("-- Hello World", return_tensors="pt")
	out = model.generate(toks, use_cache=True, do_sample=True, temperature=0.2, top_p=0.95, max_length=50)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```
	```
	-- Hello World!
	-- :param name: The name of the person to say hello to
	-- :return: A greeting
	local function say_hello(name)
	return "Hello ".. name
	end
	```