MultiPLCoder-15b

15 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.

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.

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-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.

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