Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
custom_code
text-generation-inference
Instructions to use danielv835/santacoder-finetuned-the-stack-rust-test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielv835/santacoder-finetuned-the-stack-rust-test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="danielv835/santacoder-finetuned-the-stack-rust-test1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("danielv835/santacoder-finetuned-the-stack-rust-test1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("danielv835/santacoder-finetuned-the-stack-rust-test1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use danielv835/santacoder-finetuned-the-stack-rust-test1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "danielv835/santacoder-finetuned-the-stack-rust-test1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "danielv835/santacoder-finetuned-the-stack-rust-test1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/danielv835/santacoder-finetuned-the-stack-rust-test1
- SGLang
How to use danielv835/santacoder-finetuned-the-stack-rust-test1 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 "danielv835/santacoder-finetuned-the-stack-rust-test1" \ --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": "danielv835/santacoder-finetuned-the-stack-rust-test1", "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 "danielv835/santacoder-finetuned-the-stack-rust-test1" \ --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": "danielv835/santacoder-finetuned-the-stack-rust-test1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use danielv835/santacoder-finetuned-the-stack-rust-test1 with Docker Model Runner:
docker model run hf.co/danielv835/santacoder-finetuned-the-stack-rust-test1
| license: openrail | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: santacoder-finetuned-the-stack-rust-test1 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # santacoder-finetuned-the-stack-rust-test1 | |
| This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.2870 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 3 | |
| - training_steps: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.6679 | 0.3 | 3 | 1.2840 | | |
| | 1.5399 | 0.6 | 6 | 1.3103 | | |
| | 0.3854 | 0.9 | 9 | 1.2870 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.0 | |
| - Tokenizers 0.13.2 | |