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 { mkdirSync, readFileSync, writeFileSync } from 'node:fs'; | |
| import { dirname, resolve } from 'node:path'; | |
| import { fileURLToPath } from 'node:url'; | |
| const HERE = dirname(fileURLToPath(import.meta.url)); | |
| const PROJECT_ROOT = resolve(HERE, '..'); | |
| const DEFAULT_LOGO = resolve(PROJECT_ROOT, 'src/lib/assets/logo.svg'); | |
| const DEFAULT_OUT_DIR = resolve(PROJECT_ROOT, 'static'); | |
| const DEFAULT_OUT_LIGHT = resolve(DEFAULT_OUT_DIR, 'favicon.svg'); | |
| const DEFAULT_OUT_DARK = resolve(DEFAULT_OUT_DIR, 'favicon-dark.svg'); | |
| const CURRENT_COLOR = 'currentColor'; | |
| export interface ColorizedFavicon { | |
| light: string; | |
| dark: string; | |
| } | |
| export interface WriteThemeFaviconsOptions { | |
| sourcePath?: string; | |
| lightOutPath?: string; | |
| darkOutPath?: string; | |
| /** | |
| * Fraction of the icon (0..1) to leave as an even margin on each side. | |
| * Applied by wrapping the inner content in a `<g transform="...">` so the | |
| * source `src/lib/assets/logo.svg` is not modified. Pass 0 to disable. | |
| */ | |
| padding?: number; | |
| } | |
| /** | |
| * Replace every `currentColor` occurrence in the SVG with the given color. | |
| * Pure: no filesystem access, so it is straightforward to unit-test. | |
| */ | |
| export function colorizeFaviconSvg( | |
| svg: string, | |
| lightColor: string, | |
| darkColor: string | |
| ): ColorizedFavicon { | |
| return { | |
| light: svg.replaceAll(CURRENT_COLOR, lightColor), | |
| dark: svg.replaceAll(CURRENT_COLOR, darkColor) | |
| }; | |
| } | |
| /** | |
| * Shrink the inner SVG content uniformly and re-center it so `padding` (a | |
| * 0..1 fraction) is reserved as equal margin on each side. Returns the input | |
| * unchanged for non-positive padding, missing/invalid `viewBox`, or unexpected | |
| * markup so the caller always gets a renderable SVG. | |
| */ | |
| export function padFaviconSvg(svg: string, padding: number): string { | |
| if (!(padding > 0) || padding >= 1) return svg; | |
| const viewBoxMatch = svg.match(/viewBox\s*=\s*["']([^"']+)["']/i); | |
| if (!viewBoxMatch) return svg; | |
| const parts = viewBoxMatch[1] | |
| .trim() | |
| .split(/[\s,]+/) | |
| .map(Number); | |
| if (parts.length !== 4 || parts.some((n) => !Number.isFinite(n))) return svg; | |
| const [, , width, height] = parts; | |
| if (width <= 0 || height <= 0) return svg; | |
| const scale = 1 - padding; | |
| const translateX = (padding * width) / 2; | |
| const translateY = (padding * height) / 2; | |
| const openTagStart = svg.search(/<svg\b/i); | |
| if (openTagStart === -1) return svg; | |
| const openTagEnd = svg.indexOf('>', openTagStart); | |
| if (openTagEnd === -1) return svg; | |
| const closeStart = svg.lastIndexOf('</svg'); | |
| if (closeStart === -1 || closeStart <= openTagEnd) return svg; | |
| const openTag = svg.slice(0, openTagEnd + 1); | |
| const inner = svg.slice(openTagEnd + 1, closeStart); | |
| const closeTag = svg.slice(closeStart); | |
| const group = `<g transform="translate(${translateX} ${translateY}) scale(${scale})">`; | |
| return `${openTag}${group}${inner}</g>${closeTag}`; | |
| } | |
| /** | |
| * Read `src/lib/assets/logo.svg`, colorize it for both themes, and write | |
| * the results to the static directory so the PWA asset generator can consume | |
| * them. Paths can be overridden for tests. | |
| */ | |
| export function writeThemeFavicons( | |
| lightColor: string, | |
| darkColor: string, | |
| { | |
| sourcePath = DEFAULT_LOGO, | |
| lightOutPath = DEFAULT_OUT_LIGHT, | |
| darkOutPath = DEFAULT_OUT_DARK, | |
| padding = 0 | |
| }: WriteThemeFaviconsOptions = {} | |
| ): void { | |
| const source = readFileSync(sourcePath, 'utf-8'); | |
| const { light, dark } = colorizeFaviconSvg(source, lightColor, darkColor); | |
| mkdirSync(dirname(lightOutPath), { recursive: true }); | |
| writeFileSync(lightOutPath, padFaviconSvg(light, padding)); | |
| writeFileSync(darkOutPath, padFaviconSvg(dark, padding)); | |
| } | |