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
| import { MimeTypeImage } from '$lib/enums'; | |
| // File extension patterns for resource type detection | |
| export const IMAGE_FILE_EXTENSION_REGEX = /\.(png|jpg|jpeg|gif|svg|webp)$/i; | |
| export const CODE_FILE_EXTENSION_REGEX = | |
| /\.(js|ts|json|yaml|yml|xml|html|css|py|rs|go|java|cpp|c|h|rb|sh|toml)$/i; | |
| export const TEXT_FILE_EXTENSION_REGEX = /\.(txt|md|log)$/i; | |
| // URI protocol prefix pattern | |
| export const PROTOCOL_PREFIX_REGEX = /^[a-z]+:\/\//; | |
| // File extension regex for display name extraction | |
| export const FILE_EXTENSION_REGEX = /\.[^.]+$/; | |
| // Separator regex for splitting display names (kebab-case/snake_case) | |
| export const DISPLAY_NAME_SEPARATOR_REGEX = /[-_]/; | |
| // Regex for matching base64-encoded data URIs | |
| export const DATA_URI_BASE64_REGEX = /^data:([^;]+);base64,([A-Za-z0-9+/]+=*)$/; | |
| // Prefix for MCP attachment filenames | |
| export const MCP_ATTACHMENT_NAME_PREFIX = 'mcp-attachment'; | |
| // Prefix for MCP resource attachment IDs | |
| export const MCP_RESOURCE_ATTACHMENT_ID_PREFIX = 'res'; | |
| // Default file extension for unknown image types | |
| export const DEFAULT_IMAGE_EXTENSION = 'img'; | |
| // Default filename for resource content downloads | |
| export const DEFAULT_RESOURCE_FILENAME = 'resource.txt'; | |
| // Path separator for resource URI parsing | |
| export const PATH_SEPARATOR = '/'; | |
| // Separator for joining text content from multiple resource parts | |
| export const RESOURCE_TEXT_CONTENT_SEPARATOR = '\n\n'; | |
| // Fallback text for unknown content types | |
| export const RESOURCE_UNKNOWN_TYPE = 'unknown type'; | |
| // Label prefix for binary blob content | |
| export const BINARY_CONTENT_LABEL = 'Binary content'; | |
| /** | |
| * Mapping from image MIME types to file extensions. | |
| * Used for generating attachment filenames from MIME types. | |
| */ | |
| export const IMAGE_MIME_TO_EXTENSION: Record<string, string> = { | |
| [MimeTypeImage.JPEG]: 'jpg', | |
| [MimeTypeImage.JPG]: 'jpg', | |
| [MimeTypeImage.PNG]: 'png', | |
| [MimeTypeImage.GIF]: 'gif', | |
| [MimeTypeImage.WEBP]: 'webp' | |
| } as const; | |