Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tda45/TdAI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tda45/TdAI",
	filename="llama.cpp/models/ggml-vocab-aquila.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 tda45/TdAI with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tda45/TdAI
# Run inference directly in the terminal:
llama cli -hf tda45/TdAI

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tda45/TdAI
# Run inference directly in the terminal:
llama cli -hf tda45/TdAI

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 tda45/TdAI
# Run inference directly in the terminal:
./llama-cli -hf tda45/TdAI

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 tda45/TdAI
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tda45/TdAI

Use Docker

docker model run hf.co/tda45/TdAI

LM Studio
Jan
Ollama
How to use tda45/TdAI with Ollama:
```
ollama run hf.co/tda45/TdAI
```

Unsloth Studio

How to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tda45/TdAI to start chatting

Atomic Chat new
Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
```
docker model run hf.co/tda45/TdAI
```

Lemonade

How to use tda45/TdAI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tda45/TdAI

Run and chat with the model

lemonade run user.TdAI-{{QUANT_TAG}}

List all available models

lemonade list

TdAI / llama.cpp /tools /ui /docs /flows /data-flow-simplified-model-mode.md

tda45

Upload folder using huggingface_hub (part 7)

8efb28e verified 11 days ago

preview code

Raw

History Blame Contribute Delete

1.32 kB

%% MODEL Mode Data Flow (single model)
%% Detailed flows: ./flows/server-flow.mmd, ./flows/models-flow.mmd, ./flows/chat-flow.mmd

sequenceDiagram
    participant User as 👤 User
    participant UI as 🧩 UI
    participant Stores as 🗄️ Stores
    participant DB as 💾 IndexedDB
    participant API as 🌐 llama-server

    Note over User,API: 🚀 Initialization (see: server-flow.mmd, models-flow.mmd)

    UI->>Stores: initialize()
    Stores->>DB: load conversations
    Stores->>API: GET /props
    API-->>Stores: server config + modalities
    Stores->>API: GET /v1/models
    API-->>Stores: single model (auto-selected)

    Note over User,API: 💬 Chat Flow (see: chat-flow.mmd)

    User->>UI: send message
    UI->>Stores: sendMessage()
    Stores->>DB: save user message
    Stores->>API: POST /v1/chat/completions (stream)
    loop streaming
        API-->>Stores: SSE chunks
        Stores-->>UI: reactive update
    end
    API-->>Stores: done + timings
    Stores->>DB: save assistant message

    Note over User,API: 🔁 Regenerate

    User->>UI: regenerate
    Stores->>DB: create message branch
    Note right of Stores: same streaming flow

    Note over User,API: ⏹️ Stop

    User->>UI: stop
    Stores->>Stores: abort stream
    Stores->>DB: save partial response