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RichardErkhov/
nuprl_-_MultiPL-T-StarCoderBase_1b-gguf

Name: nuprl_-_MultiPL-T-StarCoderBase_1b-gguf
Availability: InStock

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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/

Name	Quant method	Size
MultiPL-T-StarCoderBase_1b.Q2_K.gguf	Q2_K	0.5GB
MultiPL-T-StarCoderBase_1b.IQ3_XS.gguf	IQ3_XS	0.54GB
MultiPL-T-StarCoderBase_1b.IQ3_S.gguf	IQ3_S	0.55GB
MultiPL-T-StarCoderBase_1b.Q3_K_S.gguf	Q3_K_S	0.55GB
MultiPL-T-StarCoderBase_1b.IQ3_M.gguf	IQ3_M	0.58GB
MultiPL-T-StarCoderBase_1b.Q3_K.gguf	Q3_K	0.63GB
MultiPL-T-StarCoderBase_1b.Q3_K_M.gguf	Q3_K_M	0.63GB
MultiPL-T-StarCoderBase_1b.Q3_K_L.gguf	Q3_K_L	0.69GB
MultiPL-T-StarCoderBase_1b.IQ4_XS.gguf	IQ4_XS	0.65GB
MultiPL-T-StarCoderBase_1b.Q4_0.gguf	Q4_0	0.68GB
MultiPL-T-StarCoderBase_1b.IQ4_NL.gguf	IQ4_NL	0.68GB
MultiPL-T-StarCoderBase_1b.Q4_K_S.gguf	Q4_K_S	0.68GB
MultiPL-T-StarCoderBase_1b.Q4_K.gguf	Q4_K	0.74GB
MultiPL-T-StarCoderBase_1b.Q4_K_M.gguf	Q4_K_M	0.74GB
MultiPL-T-StarCoderBase_1b.Q4_1.gguf	Q4_1	0.74GB
MultiPL-T-StarCoderBase_1b.Q5_0.gguf	Q5_0	0.8GB
MultiPL-T-StarCoderBase_1b.Q5_K_S.gguf	Q5_K_S	0.8GB
MultiPL-T-StarCoderBase_1b.Q5_K.gguf	Q5_K	0.83GB
MultiPL-T-StarCoderBase_1b.Q5_K_M.gguf	Q5_K_M	0.83GB
MultiPL-T-StarCoderBase_1b.Q5_1.gguf	Q5_1	0.85GB
MultiPL-T-StarCoderBase_1b.Q6_K.gguf	Q6_K	0.92GB
MultiPL-T-StarCoderBase_1b.Q8_0.gguf	Q8_0	1.17GB

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

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.

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:

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

RichardErkhov/nuprl_-_MultiPL-T-StarCoderBase_1b-gguf

Original model description:

MultiPLCoder-1b

Language Revision Index

Usage

RichardErkhov/
nuprl_-_MultiPL-T-StarCoderBase_1b-gguf