Instructions to use houssamDev/WikitextGenModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use houssamDev/WikitextGenModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="houssamDev/WikitextGenModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("houssamDev/WikitextGenModel") model = AutoModelForCausalLM.from_pretrained("houssamDev/WikitextGenModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use houssamDev/WikitextGenModel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "houssamDev/WikitextGenModel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "houssamDev/WikitextGenModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/houssamDev/WikitextGenModel
- SGLang
How to use houssamDev/WikitextGenModel 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 "houssamDev/WikitextGenModel" \ --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": "houssamDev/WikitextGenModel", "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 "houssamDev/WikitextGenModel" \ --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": "houssamDev/WikitextGenModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use houssamDev/WikitextGenModel with Docker Model Runner:
docker model run hf.co/houssamDev/WikitextGenModel
Commit History
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Update README.md
Verified signature🧠 Text Summarization Model Evaluation This project evaluates a sequence-to-sequence Transformer model on the Wikitext-103 dataset using ROUGE metrics. The model was trained to perform abstractive text summarization. 🏋️ Training Performance Training Loss: 3.4396 This indicates the average model loss during training, showing reasonable convergence. 🧪 Validation Results Metric Score ROUGE-1 0.8325 ROUGE-2 0.7163 ROUGE-L 0.8326 ROUGE-Lsum 0.8326 The high ROUGE scores on the validation set demonstrate that the model captures both unigram and bigram overlap effectively, while maintaining structural similarity with the target summaries. 🧾 Test Results Metric Score ROUGE-1 0.7806 ROUGE-2 0.6820 ROUGE-L 0.7805 ROUGE-Lsum 0.7805 The model generalizes well to unseen data with a slight drop compared to validation performance, which is expected. 📌 Notes Model: You can replace this with your specific model name (e.g., t5-base, bart-large, etc.) Dataset: wikitext-103-raw-v1 from Hugging …7ec3d53 -
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