Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use ZL0818/my_awesome_eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZL0818/my_awesome_eli5_clm-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZL0818/my_awesome_eli5_clm-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ZL0818/my_awesome_eli5_clm-model") model = AutoModelForCausalLM.from_pretrained("ZL0818/my_awesome_eli5_clm-model") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ZL0818/my_awesome_eli5_clm-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZL0818/my_awesome_eli5_clm-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZL0818/my_awesome_eli5_clm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZL0818/my_awesome_eli5_clm-model
- SGLang
How to use ZL0818/my_awesome_eli5_clm-model 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 "ZL0818/my_awesome_eli5_clm-model" \ --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": "ZL0818/my_awesome_eli5_clm-model", "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 "ZL0818/my_awesome_eli5_clm-model" \ --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": "ZL0818/my_awesome_eli5_clm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZL0818/my_awesome_eli5_clm-model with Docker Model Runner:
docker model run hf.co/ZL0818/my_awesome_eli5_clm-model
my_awesome_eli5_clm-model
This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.7706
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.0085 | 0.13 | 500 | 3.8586 |
| 3.9418 | 0.25 | 1000 | 3.8368 |
| 3.9257 | 0.38 | 1500 | 3.8236 |
| 3.9012 | 0.51 | 2000 | 3.8139 |
| 3.9131 | 0.63 | 2500 | 3.8052 |
| 3.8947 | 0.76 | 3000 | 3.7976 |
| 3.8943 | 0.88 | 3500 | 3.7912 |
| 3.8809 | 1.01 | 4000 | 3.7887 |
| 3.8243 | 1.14 | 4500 | 3.7877 |
| 3.8251 | 1.26 | 5000 | 3.7854 |
| 3.822 | 1.39 | 5500 | 3.7824 |
| 3.8141 | 1.52 | 6000 | 3.7808 |
| 3.8243 | 1.64 | 6500 | 3.7785 |
| 3.8108 | 1.77 | 7000 | 3.7762 |
| 3.8059 | 1.89 | 7500 | 3.7755 |
| 3.7984 | 2.02 | 8000 | 3.7765 |
| 3.7866 | 2.15 | 8500 | 3.7747 |
| 3.7761 | 2.27 | 9000 | 3.7746 |
| 3.7764 | 2.4 | 9500 | 3.7727 |
| 3.779 | 2.53 | 10000 | 3.7727 |
| 3.7744 | 2.65 | 10500 | 3.7719 |
| 3.7685 | 2.78 | 11000 | 3.7708 |
| 3.7694 | 2.9 | 11500 | 3.7706 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for ZL0818/my_awesome_eli5_clm-model
Base model
distilbert/distilgpt2