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.
- 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 Desktop
- 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
- Atomic Chat
File size: 1,940 Bytes
15c3607 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | 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;
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