Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
| /** | |
| * Connection lifecycle phases for MCP protocol | |
| */ | |
| export enum MCPConnectionPhase { | |
| IDLE = 'idle', | |
| TRANSPORT_CREATING = 'transport_creating', | |
| TRANSPORT_READY = 'transport_ready', | |
| INITIALIZING = 'initializing', | |
| CAPABILITIES_EXCHANGED = 'capabilities_exchanged', | |
| LISTING_TOOLS = 'listing_tools', | |
| CONNECTED = 'connected', | |
| ERROR = 'error', | |
| DISCONNECTED = 'disconnected' | |
| } | |
| /** | |
| * Log level for connection events | |
| */ | |
| export enum MCPLogLevel { | |
| INFO = 'info', | |
| WARN = 'warn', | |
| ERROR = 'error' | |
| } | |
| /** | |
| * Transport types for MCP connections | |
| */ | |
| export enum MCPTransportType { | |
| WEBSOCKET = 'websocket', | |
| STREAMABLE_HTTP = 'streamable_http', | |
| SSE = 'sse' | |
| } | |
| /** | |
| * Health check status for MCP servers | |
| */ | |
| export enum HealthCheckStatus { | |
| IDLE = 'idle', | |
| CONNECTING = 'connecting', | |
| SUCCESS = 'success', | |
| ERROR = 'error' | |
| } | |
| /** | |
| * Content types for MCP tool results | |
| */ | |
| export enum MCPContentType { | |
| TEXT = 'text', | |
| IMAGE = 'image', | |
| RESOURCE = 'resource' | |
| } | |
| /** | |
| * JSON Schema types used in MCP tool definitions | |
| */ | |
| export enum JsonSchemaType { | |
| OBJECT = 'object', | |
| STRING = 'string', | |
| NUMBER = 'number' | |
| } | |
| /** | |
| * Reference types for MCP completions | |
| */ | |
| export enum MCPRefType { | |
| PROMPT = 'ref/prompt', | |
| RESOURCE = 'ref/resource' | |
| } | |