Instructions to use RojanSapkota/FlashGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RojanSapkota/FlashGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RojanSapkota/FlashGPT")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RojanSapkota/FlashGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RojanSapkota/FlashGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RojanSapkota/FlashGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RojanSapkota/FlashGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RojanSapkota/FlashGPT
- SGLang
How to use RojanSapkota/FlashGPT 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 "RojanSapkota/FlashGPT" \ --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": "RojanSapkota/FlashGPT", "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 "RojanSapkota/FlashGPT" \ --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": "RojanSapkota/FlashGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RojanSapkota/FlashGPT with Docker Model Runner:
docker model run hf.co/RojanSapkota/FlashGPT
FlashGPT
FlashGPT is a cutting-edge language model designed for high-speed response and precision, built using datasets from Microsoft Llama, Gemma, and Mistral. This model is optimized for various applications, providing quick and accurate outputs across multiple languages.
๐ Visit the FlashGPT Website
Key Features
- High Performance: Delivers rapid response times, ideal for latency-sensitive applications.
- Multilingual Support: Capable of understanding and generating text in multiple languages.
- Extended Context Length: Supports detailed conversations and complex tasks.
- Robust Safety Protocols: Trained with safety in mind to minimize harmful outputs.
Model Details
FlashGPT combines the strengths of datasets from multiple sources to deliver high-quality text generation, fine-tuned with supervised techniques and reinforcement learning from human feedback.
Performance Benchmarks
| Task | FlashGPT | Competitor A | Competitor B |
|---|---|---|---|
| Reasoning | 78.7 | 72.2 | 70.5 |
| Language Understanding | 71.8 | 67.0 | 62.9 |
| Code Generation | 65.3 | 60.8 | 58.4 |
Intended Use Cases
FlashGPT is suitable for:
- Memory/compute-constrained environments
- Latency-bound scenarios
- Complex reasoning tasks
Limitations
FlashGPT may not be suitable for all applications, particularly those requiring high accuracy in sensitive domains.