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--- |
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license: cc-by-sa-4.0 |
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datasets: |
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- bigcode/the-stack-dedup |
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- sadiqj/opam-source |
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tags: |
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- code |
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language: |
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- code |
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programming_language: |
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- OCaml |
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--- |
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# camlcoder |
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## Model Description |
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`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](https://www.huggingface.co/replit/replit-code-v1-3b). The model has been trained on a subset of the [Stack Dedup v1.2 dataset](https://arxiv.org/abs/2211.15533) and the most recent version of [all packages in Opam that compile on OCaml 5.0](https://www.huggingface.com/sadiqj/opam-source). |
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## License |
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The model checkpoint and vocabulary file are licensed under the Creative Commons license (CC BY-SA-4.0). |
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## Contact |
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For questions and comments about the model, please post in the community section. |
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## How to Use |
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First of all, you need to install the latest versions of the following dependencies: |
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``` |
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einops |
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sentencepiece |
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safetensors |
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torch |
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transformers |
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``` |
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You can then use the model as follows: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, StoppingCriteria, StoppingCriteriaList |
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import torch |
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max_length = 256 |
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tokenizer = AutoTokenizer.from_pretrained('sadiqj/camlcoder', trust_remote_code=True, max_length=max_length, use_safetensors=True) |
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model = AutoModelForCausalLM.from_pretrained('sadiqj/camlcoder', trust_remote_code=True, use_safetensors=True).to(device='cuda:0', dtype=torch.bfloat16) |
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input_ids = tokenizer.encode('(* Return the middle element of the list *)\nlet get_middle l =', return_tensors='pt').to(device='cuda:0') |
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newline_id = tokenizer.encode('\n\n', return_tensors='pt')[0][0].item() |
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class StopOnNewlines(StoppingCriteria): |
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
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return newline_id in input_ids |
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output = model.generate(input_ids, max_length=max_length, stopping_criteria=StoppingCriteriaList([StopOnNewlines()]), use_cache=True) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
