Instructions to use fpadovani/cds_sh1_fr_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/cds_sh1_fr_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/cds_sh1_fr_30")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/cds_sh1_fr_30") model = AutoModelForCausalLM.from_pretrained("fpadovani/cds_sh1_fr_30") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fpadovani/cds_sh1_fr_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_fr_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_fr_30", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/cds_sh1_fr_30
- SGLang
How to use fpadovani/cds_sh1_fr_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_fr_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_fr_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_fr_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_fr_30", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/cds_sh1_fr_30 with Docker Model Runner:
docker model run hf.co/fpadovani/cds_sh1_fr_30
cds_sh1_fr_30
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.3099
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 |
|---|---|---|---|
| 6.0017 | 1.0 | 145 | 4.5165 |
| 4.1747 | 2.0 | 290 | 4.0281 |
| 3.8777 | 3.0 | 435 | 3.7704 |
| 3.6723 | 4.0 | 580 | 3.6043 |
| 3.5366 | 5.0 | 725 | 3.5114 |
| 3.4452 | 6.0 | 870 | 3.4480 |
| 3.3743 | 7.0 | 1015 | 3.4009 |
| 3.3153 | 8.0 | 1160 | 3.3667 |
| 3.2657 | 9.0 | 1305 | 3.3414 |
| 3.2193 | 10.0 | 1450 | 3.3189 |
| 3.1773 | 11.0 | 1595 | 3.3084 |
| 3.1366 | 12.0 | 1740 | 3.2952 |
| 3.0985 | 13.0 | 1885 | 3.2936 |
| 3.061 | 14.0 | 2030 | 3.2873 |
| 3.0252 | 15.0 | 2175 | 3.2956 |
| 2.9897 | 16.0 | 2320 | 3.2938 |
| 2.9574 | 17.0 | 2465 | 3.2979 |
| 2.9302 | 18.0 | 2610 | 3.3015 |
| 2.9063 | 19.0 | 2755 | 3.3071 |
| 2.8895 | 20.0 | 2900 | 3.3099 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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