Instructions to use tripplet-research/synthara-legacy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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

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 — 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 — 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 — 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: 97

Safetensors

Model size

51.5M params

Tensor type

F32