Instructions to use fpadovani/bnc_o_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/bnc_o_13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/bnc_o_13")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/bnc_o_13") model = AutoModelForCausalLM.from_pretrained("fpadovani/bnc_o_13") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fpadovani/bnc_o_13 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fpadovani/bnc_o_13" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fpadovani/bnc_o_13", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/bnc_o_13
- SGLang
How to use fpadovani/bnc_o_13 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/bnc_o_13" \ --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/bnc_o_13", "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/bnc_o_13" \ --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/bnc_o_13", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/bnc_o_13 with Docker Model Runner:
docker model run hf.co/fpadovani/bnc_o_13
bnc_o_13
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.3431
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: 13
- 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 |
|---|---|---|---|
| 6.8358 | 1.0 | 391 | 5.7443 |
| 5.4018 | 2.0 | 782 | 5.1832 |
| 5.0084 | 3.0 | 1173 | 4.9334 |
| 4.784 | 4.0 | 1564 | 4.7744 |
| 4.6185 | 5.0 | 1955 | 4.6605 |
| 4.484 | 6.0 | 2346 | 4.5739 |
| 4.3687 | 7.0 | 2737 | 4.5070 |
| 4.2672 | 8.0 | 3128 | 4.4554 |
| 4.1753 | 9.0 | 3519 | 4.4167 |
| 4.0922 | 10.0 | 3910 | 4.3882 |
| 4.0179 | 11.0 | 4301 | 4.3677 |
| 3.9516 | 12.0 | 4692 | 4.3529 |
| 3.8915 | 13.0 | 5083 | 4.3446 |
| 3.8371 | 14.0 | 5474 | 4.3404 |
| 3.7888 | 15.0 | 5865 | 4.3370 |
| 3.7454 | 16.0 | 6256 | 4.3368 |
| 3.7072 | 17.0 | 6647 | 4.3382 |
| 3.6747 | 18.0 | 7038 | 4.3401 |
| 3.6475 | 19.0 | 7429 | 4.3413 |
| 3.627 | 20.0 | 7820 | 4.3431 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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