Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use srsawant34/codeparrot-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srsawant34/codeparrot-ds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="srsawant34/codeparrot-ds")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("srsawant34/codeparrot-ds") model = AutoModelForCausalLM.from_pretrained("srsawant34/codeparrot-ds") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use srsawant34/codeparrot-ds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "srsawant34/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": "srsawant34/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/srsawant34/codeparrot-ds
- SGLang
How to use srsawant34/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 "srsawant34/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": "srsawant34/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 "srsawant34/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": "srsawant34/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use srsawant34/codeparrot-ds with Docker Model Runner:
docker model run hf.co/srsawant34/codeparrot-ds
codeparrot-ds
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9628
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: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.8291 | 0.09 | 500 | 3.1048 |
| 2.7568 | 0.19 | 1000 | 2.6598 |
| 2.4536 | 0.28 | 1500 | 2.4579 |
| 2.284 | 0.38 | 2000 | 2.3267 |
| 2.1605 | 0.47 | 2500 | 2.2221 |
| 2.0615 | 0.56 | 3000 | 2.1385 |
| 1.9799 | 0.66 | 3500 | 2.0642 |
| 1.9192 | 0.75 | 4000 | 2.0100 |
| 1.8725 | 0.84 | 4500 | 1.9766 |
| 1.8484 | 0.94 | 5000 | 1.9628 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for srsawant34/codeparrot-ds
Base model
distilbert/distilgpt2