Instructions to use fpadovani/candor_np_67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/candor_np_67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/candor_np_67")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/candor_np_67") model = AutoModelForCausalLM.from_pretrained("fpadovani/candor_np_67") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fpadovani/candor_np_67 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fpadovani/candor_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/candor_np_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/candor_np_67
- SGLang
How to use fpadovani/candor_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/candor_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/candor_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/candor_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/candor_np_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/candor_np_67 with Docker Model Runner:
docker model run hf.co/fpadovani/candor_np_67
candor_np_67
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.4619
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 |
|---|---|---|---|
| 5.7451 | 1.0 | 422 | 4.7857 |
| 4.631 | 2.0 | 844 | 4.6000 |
| 4.5095 | 3.0 | 1266 | 4.5222 |
| 4.4382 | 4.0 | 1688 | 4.4741 |
| 4.3845 | 5.0 | 2110 | 4.4414 |
| 4.3384 | 6.0 | 2532 | 4.4156 |
| 4.2969 | 7.0 | 2954 | 4.3983 |
| 4.2572 | 8.0 | 3376 | 4.3870 |
| 4.2185 | 9.0 | 3798 | 4.3797 |
| 4.1799 | 10.0 | 4220 | 4.3767 |
| 4.1409 | 11.0 | 4642 | 4.3787 |
| 4.1018 | 12.0 | 5064 | 4.3833 |
| 4.0629 | 13.0 | 5486 | 4.3915 |
| 4.0248 | 14.0 | 5908 | 4.4019 |
| 3.988 | 15.0 | 6330 | 4.4138 |
| 3.9537 | 16.0 | 6752 | 4.4256 |
| 3.9217 | 17.0 | 7174 | 4.4394 |
| 3.894 | 18.0 | 7596 | 4.4476 |
| 3.8717 | 19.0 | 8018 | 4.4576 |
| 3.8552 | 20.0 | 8440 | 4.4619 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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