camlcoder
Model Description
camlcoder is a 2.7B Causal Language Model focused on Code Completion for OCaml. It is a fine-tuned version of replit-code-v1-3b. The model has been trained on a subset of the Stack Dedup v1.2 dataset and the most recent version of all packages in Opam that compile on OCaml 5.0.
License
The model checkpoint and vocabulary file are licensed under the Creative Commons license (CC BY-SA-4.0).
Contact
For questions and comments about the model, please post in the community section.
How to Use
First of all, you need to install the latest versions of the following dependencies:
einops
sentencepiece
safetensors
torch
transformers
You can then use the model as follows:
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, StoppingCriteria, StoppingCriteriaList
import torch
max_length = 256
tokenizer = AutoTokenizer.from_pretrained('sadiqj/camlcoder', trust_remote_code=True, max_length=max_length, use_safetensors=True)
model = AutoModelForCausalLM.from_pretrained('sadiqj/camlcoder', trust_remote_code=True, use_safetensors=True).to(device='cuda:0', dtype=torch.bfloat16)
input_ids = tokenizer.encode('(* Return the middle element of the list *)\nlet get_middle l =', return_tensors='pt').to(device='cuda:0')
newline_id = tokenizer.encode('\n\n', return_tensors='pt')[0][0].item()
class StopOnNewlines(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return newline_id in input_ids
output = model.generate(input_ids, max_length=max_length, stopping_criteria=StoppingCriteriaList([StopOnNewlines()]), use_cache=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))