Instructions to use NasimB/cl-rairty-138k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/cl-rairty-138k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NasimB/cl-rairty-138k")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NasimB/cl-rairty-138k") model = AutoModelForCausalLM.from_pretrained("NasimB/cl-rairty-138k") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NasimB/cl-rairty-138k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NasimB/cl-rairty-138k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NasimB/cl-rairty-138k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NasimB/cl-rairty-138k
- SGLang
How to use NasimB/cl-rairty-138k 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/cl-rairty-138k" \ --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/cl-rairty-138k", "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/cl-rairty-138k" \ --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/cl-rairty-138k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NasimB/cl-rairty-138k with Docker Model Runner:
docker model run hf.co/NasimB/cl-rairty-138k
cl-rairty-138k
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 4.5428
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: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.2839 | 0.05 | 500 | 5.4795 |
| 5.0415 | 0.11 | 1000 | 5.1006 |
| 4.7226 | 0.16 | 1500 | 4.9120 |
| 4.5104 | 0.22 | 2000 | 4.8065 |
| 4.3612 | 0.27 | 2500 | 4.7228 |
| 4.2428 | 0.33 | 3000 | 4.6795 |
| 4.1319 | 0.38 | 3500 | 4.6186 |
| 4.0383 | 0.44 | 4000 | 4.5901 |
| 3.9574 | 0.49 | 4500 | 4.5596 |
| 3.8673 | 0.55 | 5000 | 4.5309 |
| 3.7879 | 0.6 | 5500 | 4.5100 |
| 3.7136 | 0.66 | 6000 | 4.4966 |
| 3.6418 | 0.71 | 6500 | 4.4850 |
| 3.5814 | 0.76 | 7000 | 4.4735 |
| 3.5361 | 0.82 | 7500 | 4.4643 |
| 3.4948 | 0.87 | 8000 | 4.4619 |
| 3.477 | 0.93 | 8500 | 4.4579 |
| 3.4652 | 0.98 | 9000 | 4.4568 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
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