Instructions to use NasimB/aochildes-len with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/aochildes-len with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NasimB/aochildes-len")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NasimB/aochildes-len") model = AutoModelForCausalLM.from_pretrained("NasimB/aochildes-len") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NasimB/aochildes-len with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NasimB/aochildes-len" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NasimB/aochildes-len", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NasimB/aochildes-len
- SGLang
How to use NasimB/aochildes-len 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 "NasimB/aochildes-len" \ --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": "NasimB/aochildes-len", "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 "NasimB/aochildes-len" \ --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": "NasimB/aochildes-len", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NasimB/aochildes-len with Docker Model Runner:
docker model run hf.co/NasimB/aochildes-len
aochildes-len
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 4.0766
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.3523 | 0.29 | 500 | 5.3179 |
| 5.0501 | 0.58 | 1000 | 4.9002 |
| 4.722 | 0.87 | 1500 | 4.6623 |
| 4.4583 | 1.16 | 2000 | 4.5189 |
| 4.2995 | 1.46 | 2500 | 4.4057 |
| 4.2054 | 1.75 | 3000 | 4.3019 |
| 4.0894 | 2.04 | 3500 | 4.2295 |
| 3.899 | 2.33 | 4000 | 4.1853 |
| 3.8723 | 2.62 | 4500 | 4.1366 |
| 3.8354 | 2.91 | 5000 | 4.0862 |
| 3.6445 | 3.2 | 5500 | 4.0826 |
| 3.5864 | 3.49 | 6000 | 4.0549 |
| 3.5735 | 3.79 | 6500 | 4.0269 |
| 3.4938 | 4.08 | 7000 | 4.0250 |
| 3.3154 | 4.37 | 7500 | 4.0210 |
| 3.3128 | 4.66 | 8000 | 4.0103 |
| 3.3064 | 4.95 | 8500 | 3.9989 |
| 3.1572 | 5.24 | 9000 | 4.0141 |
| 3.1295 | 5.53 | 9500 | 4.0134 |
| 3.1369 | 5.82 | 10000 | 4.0127 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
- Downloads last month
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docker model run hf.co/NasimB/aochildes-len