nuprl_-_MultiPL-T-StarCoderBase_1b-gguf

Quantization made by Richard Erkhov.

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MultiPL-T-StarCoderBase_1b - GGUF

• Model creator: https://huggingface.co/nuprl/
• Original model: https://huggingface.co/nuprl/MultiPL-T-StarCoderBase_1b/

Original model description:

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

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MultiPLCoder-1b

1 billion parameter version of MultiPLCoder, a set of StarCoder-based models finetuned on the MultiPL-T dataset. 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.

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:

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.

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