metadata
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