Text Generation
Transformers
English
ai-personas
digital-twins
episodic-memory
emotional-continuity
session-handoff
llm-agents
self-authored-memory
persona-continuity
Instructions to use Transcrypto/yesterday-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Transcrypto/yesterday-json with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Transcrypto/yesterday-json")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Transcrypto/yesterday-json", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Transcrypto/yesterday-json with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Transcrypto/yesterday-json" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Transcrypto/yesterday-json", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Transcrypto/yesterday-json
- SGLang
How to use Transcrypto/yesterday-json 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 "Transcrypto/yesterday-json" \ --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": "Transcrypto/yesterday-json", "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 "Transcrypto/yesterday-json" \ --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": "Transcrypto/yesterday-json", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Transcrypto/yesterday-json with Docker Model Runner:
docker model run hf.co/Transcrypto/yesterday-json
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Verified signaturePrior Art Anima Core: identity persistence with emotional weighting Thane AI: self-authored context compaction Qwen Episodic Summary: structured JSON session snapshots Forge Protocol: topological emotional constraints VividnessMem: neurochemically-inspired memory decay Research Paper Full concept paper with formal schema, differentiation analysis, and empirical validation protocol: Chetan Sharma. Episodic Memory for AI Personas via Self-Authored Emotional State Snapshots: The yesterday.json Architecture. Zenodo, 15 May 2026. DOI: 10.5281/zenodo.20191876 Blog: yesterday-json.blogspot.com Citation bibtex @misc{sharma2026yesterdayjson, author = {Chetan Sharma}, title = {Episodic Memory for AI Personas via Self-Authored Emotional State Snapshots: The yesterday.json Architecture}, year = {2026}, month = may, doi = {10.5281/zenodo.20191876}, publisher = {Zenodo}, url = {https://zenodo.org/records/20191876} } Author Cheta…6ba6139 -
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