Instructions to use sadiqj/camlcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sadiqj/camlcoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sadiqj/camlcoder", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sadiqj/camlcoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sadiqj/camlcoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sadiqj/camlcoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sadiqj/camlcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sadiqj/camlcoder
- SGLang
How to use sadiqj/camlcoder with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "sadiqj/camlcoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sadiqj/camlcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "sadiqj/camlcoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sadiqj/camlcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sadiqj/camlcoder with Docker Model Runner:
docker model run hf.co/sadiqj/camlcoder
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license: cc-by-sa-4.0
datasets:
- bigcode/the-stack-dedup
- sadiqj/opam-source
tags:
- code
language:
- code
programming_language:
- OCaml
---
# 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](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).
## 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:
```python
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))
``` |