Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use kbalde/codeparrot-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbalde/codeparrot-ds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kbalde/codeparrot-ds")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kbalde/codeparrot-ds") model = AutoModelForCausalLM.from_pretrained("kbalde/codeparrot-ds") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kbalde/codeparrot-ds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kbalde/codeparrot-ds" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kbalde/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kbalde/codeparrot-ds
- SGLang
How to use kbalde/codeparrot-ds 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 "kbalde/codeparrot-ds" \ --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": "kbalde/codeparrot-ds", "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 "kbalde/codeparrot-ds" \ --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": "kbalde/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kbalde/codeparrot-ds with Docker Model Runner:
docker model run hf.co/kbalde/codeparrot-ds
codeparrot-ds
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0626
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5672 | 0.08 | 5000 | 1.7393 |
| 1.6801 | 0.15 | 10000 | 1.5217 |
| 1.5349 | 0.23 | 15000 | 1.4211 |
| 1.4557 | 0.31 | 20000 | 1.3588 |
| 1.3968 | 0.38 | 25000 | 1.3071 |
| 1.3458 | 0.46 | 30000 | 1.2592 |
| 1.2994 | 0.54 | 35000 | 1.2142 |
| 1.2541 | 0.61 | 40000 | 1.1730 |
| 1.2123 | 0.69 | 45000 | 1.1335 |
| 1.175 | 0.77 | 50000 | 1.1012 |
| 1.1454 | 0.84 | 55000 | 1.0777 |
| 1.1247 | 0.92 | 60000 | 1.0652 |
| 1.1168 | 1.0 | 65000 | 1.0626 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 5