Instructions to use fpadovani/wiki_sh1_67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpadovani/wiki_sh1_67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fpadovani/wiki_sh1_67")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fpadovani/wiki_sh1_67") model = AutoModelForCausalLM.from_pretrained("fpadovani/wiki_sh1_67") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fpadovani/wiki_sh1_67 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fpadovani/wiki_sh1_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/wiki_sh1_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fpadovani/wiki_sh1_67
- SGLang
How to use fpadovani/wiki_sh1_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/wiki_sh1_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/wiki_sh1_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/wiki_sh1_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/wiki_sh1_67", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fpadovani/wiki_sh1_67 with Docker Model Runner:
docker model run hf.co/fpadovani/wiki_sh1_67
wiki_sh1_67
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.0185
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 |
|---|---|---|---|
| 7.3511 | 1.0 | 399 | 6.7810 |
| 6.5625 | 2.0 | 798 | 6.5015 |
| 6.3574 | 3.0 | 1197 | 6.3690 |
| 6.2341 | 4.0 | 1596 | 6.2810 |
| 6.1366 | 5.0 | 1995 | 6.2110 |
| 6.0506 | 6.0 | 2394 | 6.1529 |
| 5.9708 | 7.0 | 2793 | 6.1053 |
| 5.896 | 8.0 | 3192 | 6.0643 |
| 5.8247 | 9.0 | 3591 | 6.0380 |
| 5.7573 | 10.0 | 3990 | 6.0178 |
| 5.6939 | 11.0 | 4389 | 6.0037 |
| 5.6329 | 12.0 | 4788 | 5.9960 |
| 5.5769 | 13.0 | 5187 | 5.9928 |
| 5.5238 | 14.0 | 5586 | 5.9932 |
| 5.4758 | 15.0 | 5985 | 5.9973 |
| 5.4308 | 16.0 | 6384 | 6.0017 |
| 5.3926 | 17.0 | 6783 | 6.0082 |
| 5.361 | 18.0 | 7182 | 6.0122 |
| 5.335 | 19.0 | 7581 | 6.0163 |
| 5.317 | 20.0 | 7980 | 6.0185 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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