Instructions to use fpadovani/cds_sh1_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/cds_sh1_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/cds_sh1_30")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/cds_sh1_30") model = AutoModelForCausalLM.from_pretrained("fpadovani/cds_sh1_30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fpadovani/cds_sh1_30 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fpadovani/cds_sh1_30" # 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_sh1_30", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/cds_sh1_30
- SGLang
How to use fpadovani/cds_sh1_30 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_sh1_30" \ --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_sh1_30", "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_sh1_30" \ --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_sh1_30", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/cds_sh1_30 with Docker Model Runner:
docker model run hf.co/fpadovani/cds_sh1_30
cds_sh1_30
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1615
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: 30
- 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 |
|---|---|---|---|
| 4.8958 | 1.0 | 495 | 3.9315 |
| 3.6499 | 2.0 | 990 | 3.5397 |
| 3.3793 | 3.0 | 1485 | 3.3794 |
| 3.2264 | 4.0 | 1980 | 3.2874 |
| 3.1179 | 5.0 | 2475 | 3.2294 |
| 3.0343 | 6.0 | 2970 | 3.1836 |
| 2.9631 | 7.0 | 3465 | 3.1519 |
| 2.901 | 8.0 | 3960 | 3.1372 |
| 2.8443 | 9.0 | 4455 | 3.1182 |
| 2.7925 | 10.0 | 4950 | 3.1129 |
| 2.7436 | 11.0 | 5445 | 3.1106 |
| 2.6967 | 12.0 | 5940 | 3.1145 |
| 2.6527 | 13.0 | 6435 | 3.1148 |
| 2.6122 | 14.0 | 6930 | 3.1206 |
| 2.575 | 15.0 | 7425 | 3.1303 |
| 2.542 | 16.0 | 7920 | 3.1401 |
| 2.512 | 17.0 | 8415 | 3.1443 |
| 2.4866 | 18.0 | 8910 | 3.1503 |
| 2.4648 | 19.0 | 9405 | 3.1571 |
| 2.4479 | 20.0 | 9900 | 3.1615 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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