Instructions to use fpadovani/cds_np_67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/cds_np_67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/cds_np_67")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/cds_np_67") model = AutoModelForCausalLM.from_pretrained("fpadovani/cds_np_67") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fpadovani/cds_np_67 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fpadovani/cds_np_67" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fpadovani/cds_np_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/cds_np_67
- SGLang
How to use fpadovani/cds_np_67 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 "fpadovani/cds_np_67" \ --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": "fpadovani/cds_np_67", "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 "fpadovani/cds_np_67" \ --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": "fpadovani/cds_np_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/cds_np_67 with Docker Model Runner:
docker model run hf.co/fpadovani/cds_np_67
cds_np_67
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1210
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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 67
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.8361 | 1.0 | 495 | 3.7897 |
| 3.538 | 2.0 | 990 | 3.4327 |
| 3.2785 | 3.0 | 1485 | 3.2872 |
| 3.1342 | 4.0 | 1980 | 3.2071 |
| 3.032 | 5.0 | 2475 | 3.1511 |
| 2.9514 | 6.0 | 2970 | 3.1132 |
| 2.8836 | 7.0 | 3465 | 3.0901 |
| 2.8231 | 8.0 | 3960 | 3.0736 |
| 2.7678 | 9.0 | 4455 | 3.0633 |
| 2.7164 | 10.0 | 4950 | 3.0574 |
| 2.6678 | 11.0 | 5445 | 3.0589 |
| 2.6213 | 12.0 | 5940 | 3.0614 |
| 2.5765 | 13.0 | 6435 | 3.0655 |
| 2.5352 | 14.0 | 6930 | 3.0753 |
| 2.4983 | 15.0 | 7425 | 3.0819 |
| 2.4651 | 16.0 | 7920 | 3.0909 |
| 2.4353 | 17.0 | 8415 | 3.1027 |
| 2.4096 | 18.0 | 8910 | 3.1099 |
| 2.3879 | 19.0 | 9405 | 3.1155 |
| 2.3706 | 20.0 | 9900 | 3.1210 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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