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
metadata
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
- Clone the model:
git clone https://huggingface.co/SkillForge45/CyberFuture-2
- Install all requirements:
pip install fastapi torch transformers tqdm pyttsx3 datasets speech_recognition pydantic univorn typing
Usage
- Train the model:
python model.py
- Launch the server:
python app.py
- Parse chat from server:
curl -X POST "http://localhost:8000/chat/"
- Start chatting:
-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
©2025 EidolonAI, "CyberFuture-2"