Instructions to use nuprl/MultiPL-T-StarCoderBase_15b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nuprl/MultiPL-T-StarCoderBase_15b with Transformers:

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

pipe = pipeline("text-generation", model="nuprl/MultiPL-T-StarCoderBase_15b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPL-T-StarCoderBase_15b")
model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPL-T-StarCoderBase_15b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nuprl/MultiPL-T-StarCoderBase_15b 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_15b"
# 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_15b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_15b

SGLang

How to use nuprl/MultiPL-T-StarCoderBase_15b 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_15b" \
    --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_15b",
		"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_15b" \
        --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_15b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nuprl/MultiPL-T-StarCoderBase_15b with Docker Model Runner:
```
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_15b
```

MultiPL-T-StarCoderBase_15b / README.md

arjunguha

StarCoder_15b_r_v2_checkpoint_100

534cdbf verified over 2 years ago

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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-15b-OCaml
	results:
	- task:
	type: text-generation
	dataset:
	name: MultiPL-HumanEval (Lua)
	type: nuprl/MultiPL-E
	metrics:
	- type: pass@1
	value: 0.31
	name: pass@1
	verified: true
	- type: pass@1
	value: 0.21
	name: pass@1
	verified: true
	- type: pass@1
	value: 0.199
	name: pass@1
	verified: true
	---
	# MultiPLCoder-15b

	15 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.

	This 15 billion parameter model is the most capable of the MultiPLCoder family. However, it requires a dedicated GPU for inference.
	For a smaller model that fits on the CPU, check out [MultiPLCoder-1b](https://huggingface.co/nuprl/MultiPLCoder-1b).


	## Language Revision Index

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

	\| Langauge \| Revision ID \| Epoch \|
	\| ------------- \| ----------- \| ----- \|
	\| Lua \| `6069aa54dd554404dd18fccdf5dedd56b8088e74` \| 4 \|
	\| Racket \| `f0c77c06482f436f469007f20d731cb9dd73d609` \| 8 \|
	\| OCaml \| `e7babda985786810707200ff885df6105de7dc56` \| 4 \|

	## 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-15b")
	lua_revision="6069aa54dd554404dd18fccdf5dedd56b8088e74"
	model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPLCoder-15b", revision=lua_revision).cuda()
	```

	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("-- Fibonacci iterative", return_tensors="pt").cuda()
	out = model.generate(toks, use_cache=True, do_sample=True, temperature=0.2, top_p=0.95, max_length=256)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```
	```
	-- Fibonacci iterative.
	local function fib_iterative(n)
	if n == 0 or n == 1 then
	return n
	end
	local previous, current = 0, 1
	for _ = 2, n do
	previous, current = current, current + previous
	end
	return current
	end
	```