Instructions to use SkillForge45/CyberFuture-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillForge45/CyberFuture-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SkillForge45/CyberFuture-2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SkillForge45/CyberFuture-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SkillForge45/CyberFuture-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SkillForge45/CyberFuture-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SkillForge45/CyberFuture-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SkillForge45/CyberFuture-2
- SGLang
How to use SkillForge45/CyberFuture-2 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 "SkillForge45/CyberFuture-2" \ --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": "SkillForge45/CyberFuture-2", "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 "SkillForge45/CyberFuture-2" \ --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": "SkillForge45/CyberFuture-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SkillForge45/CyberFuture-2 with Docker Model Runner:
docker model run hf.co/SkillForge45/CyberFuture-2
| license: mit | |
| datasets: | |
| - ParlAI/blended_skill_talk | |
| - convai-challenge/conv_ai_2 | |
| - allenai/social_i_qa | |
| language: | |
| - en | |
| - ru | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - perplexity | |
| - bleu | |
| - rouge | |
| base_model: | |
| - SkillForge45/CyberFuture-1 | |
| new_version: SkillForge45/CyberFuture-2 | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - legal | |
| # CyberFuture-2 - New version of CyberFuture | |
| Uptades: | |
| **-Voice & Text chat** | |
| **-App.py directly from the model.py file, not from the repository** | |
| # Installation | |
| 1. Clone the model: | |
| ```bash | |
| git clone https://huggingface.co/SkillForge45/CyberFuture-2 | |
| ``` | |
| 2. Install all requirements: | |
| ```bash | |
| pip install fastapi torch transformers tqdm pyttsx3 datasets speech_recognition pydantic univorn typing | |
| ``` | |
| # Usage | |
| 1. Train the model: | |
| ```bash | |
| python model.py | |
| ``` | |
| 2. Launch the server: | |
| ```bash | |
| python app.py | |
| ``` | |
| 3. Parse chat from server: | |
| ```bash | |
| curl -X POST "http://localhost:8000/chat/" | |
| ``` | |
| 4. Start chatting: | |
| ```bash | |
| -H "Content-Type: multipart/form-data" \ | |
| -F "prompt=YOUR_QUESTION_HERE" \ | |
| -F "max_length=50" \ | |
| -F "use_voice=false" #for text chat | |
| ### .void | |
| -H "Content-Type: multipart/form-data" \ | |
| -F "audio_file=@audio.wav" \ | |
| -F "max_length=50" \ | |
| -F "use_voice=true" #for voice chat | |
| ``` | |
| # License | |
| This model is licensed, see the "LICENSE" for more information | |
| <small><small>©2025 EidolonAI, "CyberFuture-2"<small><small> |