Instructions to use tripplet-research/synthara-legacy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tripplet-research/synthara-legacy with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tripplet-research/synthara-legacy", filename="synthara-legacy-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tripplet-research/synthara-legacy with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tripplet-research/synthara-legacy:F16 # Run inference directly in the terminal: llama-cli -hf tripplet-research/synthara-legacy:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tripplet-research/synthara-legacy:F16 # Run inference directly in the terminal: llama-cli -hf tripplet-research/synthara-legacy:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tripplet-research/synthara-legacy:F16 # Run inference directly in the terminal: ./llama-cli -hf tripplet-research/synthara-legacy:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tripplet-research/synthara-legacy:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf tripplet-research/synthara-legacy:F16
Use Docker
docker model run hf.co/tripplet-research/synthara-legacy:F16
- LM Studio
- Jan
- vLLM
How to use tripplet-research/synthara-legacy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tripplet-research/synthara-legacy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tripplet-research/synthara-legacy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tripplet-research/synthara-legacy:F16
- Ollama
How to use tripplet-research/synthara-legacy with Ollama:
ollama run hf.co/tripplet-research/synthara-legacy:F16
- Unsloth Studio new
How to use tripplet-research/synthara-legacy with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tripplet-research/synthara-legacy to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tripplet-research/synthara-legacy to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tripplet-research/synthara-legacy to start chatting
- Docker Model Runner
How to use tripplet-research/synthara-legacy with Docker Model Runner:
docker model run hf.co/tripplet-research/synthara-legacy:F16
- Lemonade
How to use tripplet-research/synthara-legacy with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tripplet-research/synthara-legacy:F16
Run and chat with the model
lemonade run user.synthara-legacy-F16
List all available models
lemonade list
Synthara Legacy
Deprecated. This model is no longer maintained and is not recommended for any production or serious research use. It exists purely as a historical artifact for the Tripplet Research organisation. See newer Synthara releases if any are available.
Honest disclaimer: Synthara Legacy is not a good model. It was built as an early proof-of-concept with randomly initialised weights and no fine-tuning on meaningful data. Output quality is poor โ expect incoherent or repetitive text. It is published here for transparency and archival purposes only.
Architecture
| Property | Value |
|---|---|
| Base architecture | GPT-2 |
| Parameters | ~51.5 M |
| Layers | 8 |
| Attention heads | 8 |
| Embedding dim | 512 |
| Context length | 1 024 tokens |
| Tokenizer | GPT-2 fast (Apache 2.0) |
Status
DEPRECATED โ do not use in production.
This checkpoint has never been trained on any dataset. Weights are random initialisations only. It will not produce useful output without significant fine-tuning.
License
Apache 2.0 โ see LICENSE.
Credits
- Architecture based on the open GPT-2 specification (OpenAI, MIT licence).
- Tokenizer from
openai-community/gpt2(MIT licence). - Built with Transformers (Apache 2.0).
- Published by Tripplet Research.
This model is not derived from any unlicensed third-party checkpoint.
- Downloads last month
- 105