Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Text generation
Generated from Trainer
text-generation-inference
Instructions to use ljb0967/codeparrot-ds-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljb0967/codeparrot-ds-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ljb0967/codeparrot-ds-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ljb0967/codeparrot-ds-2") model = AutoModelForCausalLM.from_pretrained("ljb0967/codeparrot-ds-2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ljb0967/codeparrot-ds-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ljb0967/codeparrot-ds-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ljb0967/codeparrot-ds-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ljb0967/codeparrot-ds-2
- SGLang
How to use ljb0967/codeparrot-ds-2 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 "ljb0967/codeparrot-ds-2" \ --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": "ljb0967/codeparrot-ds-2", "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 "ljb0967/codeparrot-ds-2" \ --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": "ljb0967/codeparrot-ds-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ljb0967/codeparrot-ds-2 with Docker Model Runner:
docker model run hf.co/ljb0967/codeparrot-ds-2
codeparrot-ds-2
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0614
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 |
|---|---|---|---|
| 2.5681 | 0.0766 | 5000 | 1.7468 |
| 1.6828 | 0.1533 | 10000 | 1.5245 |
| 1.5337 | 0.2299 | 15000 | 1.4208 |
| 1.4542 | 0.3065 | 20000 | 1.3556 |
| 1.3941 | 0.3832 | 25000 | 1.3040 |
| 1.3429 | 0.4598 | 30000 | 1.2564 |
| 1.2972 | 0.5365 | 35000 | 1.2130 |
| 1.2508 | 0.6131 | 40000 | 1.1707 |
| 1.21 | 0.6897 | 45000 | 1.1323 |
| 1.1723 | 0.7664 | 50000 | 1.0998 |
| 1.1453 | 0.8430 | 55000 | 1.0765 |
| 1.1239 | 0.9196 | 60000 | 1.0645 |
| 1.1165 | 0.9963 | 65000 | 1.0614 |
Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for ljb0967/codeparrot-ds-2
Base model
openai-community/gpt2