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 { readdirSync, readFileSync, writeFileSync, existsSync } from 'node:fs'; | |
| import { resolve } from 'path'; | |
| import type { Plugin } from 'vite'; | |
| import { TAB, NEWLINE } from '../src/lib/constants/code'; | |
| import { APPLE_DEVICES, BUILD_CONFIG, REGEX_PATTERNS, SPLASH_LINK } from '../src/lib/constants/pwa'; | |
| import type { SplashDimensions } from '../src/lib/types'; | |
| import { SplashOrientation } from '../src/lib/enums/splash.enums'; | |
| let processed = false; | |
| const OUTPUT_DIR = process.env.LLAMA_UI_OUT_DIR ?? BUILD_CONFIG.OUTPUT_DIR; | |
| /** | |
| * Generate iOS splash screen <link> tags from generated apple-splash-*.png files. | |
| * Returns an array of HTML link strings to be injected into the page head. | |
| */ | |
| export function generateSplashScreenLinks(outDir: string): string[] { | |
| const files = readdirSync(outDir).filter((f) => f.match(REGEX_PATTERNS.SPLASH_FILE)); | |
| if (files.length === 0) return []; | |
| const dimMap = new Map<string, SplashDimensions>(); | |
| for (const [dims, spec] of Object.entries(APPLE_DEVICES)) { | |
| const [w, h] = dims.split('x').map(Number); | |
| // logical-point dimensions | |
| dimMap.set(`${w}x${h}`, { deviceW: spec.width, deviceH: spec.height, dpr: spec.dpr }); | |
| dimMap.set(`${h}x${w}`, { deviceW: spec.width, deviceH: spec.height, dpr: spec.dpr }); | |
| // pixel dimensions (used by actual generated splash files) | |
| dimMap.set(`${w * spec.dpr}x${h * spec.dpr}`, { | |
| deviceW: spec.width, | |
| deviceH: spec.height, | |
| dpr: spec.dpr | |
| }); | |
| dimMap.set(`${h * spec.dpr}x${w * spec.dpr}`, { | |
| deviceW: spec.width, | |
| deviceH: spec.height, | |
| dpr: spec.dpr | |
| }); | |
| } | |
| const lightLinks: string[] = []; | |
| const darkLinks: string[] = []; | |
| for (const file of files) { | |
| const match = file.match(REGEX_PATTERNS.SPLASH_FILE); | |
| if (!match) continue; | |
| const orientation = match[1] as SplashOrientation; | |
| const isDark = !!match[2]; | |
| const pixelW = parseInt(match[3]); | |
| const pixelH = parseInt(match[4]); | |
| const key = `${pixelW}x${pixelH}`; | |
| const spec = dimMap.get(key); | |
| if (!spec) { | |
| console.warn(`Unknown splash screen dimensions: ${key} (${file})`); | |
| continue; | |
| } | |
| const { deviceW, deviceH, dpr } = spec; | |
| const media = `screen and (device-width: ${deviceW}px) and (device-height: ${deviceH}px) and (-webkit-device-pixel-ratio: ${dpr}) and (orientation: ${orientation})`; | |
| const href = `./${file}`; | |
| if (isDark) { | |
| darkLinks.push( | |
| `${SPLASH_LINK.HTML} media="${media}${SPLASH_LINK.DARK_MEDIA_SUFFIX}" href="${href}">` | |
| ); | |
| } else { | |
| lightLinks.push(`${SPLASH_LINK.HTML} media="${media}" href="${href}">`); | |
| } | |
| } | |
| return [...lightLinks, ...darkLinks]; | |
| } | |
| export function splashScreenPlugin(): Plugin { | |
| return { | |
| name: 'llamacpp:splash-screen', | |
| apply: 'build', | |
| closeBundle() { | |
| setTimeout(() => { | |
| try { | |
| if (processed) return; | |
| processed = true; | |
| const outDir = resolve(OUTPUT_DIR); | |
| const indexPath = resolve(outDir, 'index.html'); | |
| if (!existsSync(indexPath)) return; | |
| let content = readFileSync(indexPath, 'utf-8'); | |
| // Inject iOS splash screen <link> tags into <head>. | |
| // The @vite-pwa/assets-generator generates apple-splash-*.png files; | |
| // this scans them and creates the <link> tags SvelteKit needs. | |
| const splashLinks = generateSplashScreenLinks(outDir); | |
| if (splashLinks.length > 0) { | |
| console.log(`Generated ${splashLinks.length} apple-splash link tags`); | |
| const splashHtml = splashLinks.map((l) => TAB + TAB + l).join(NEWLINE); | |
| content = content.replace( | |
| REGEX_PATTERNS.HEAD_CLOSE, | |
| splashHtml + NEWLINE + TAB + TAB + '</head>' | |
| ); | |
| } | |
| // Remove trailing \r from Windows line endings | |
| content = content.replace(/\r/g, ''); | |
| content = BUILD_CONFIG.GUIDE_COMMENT + NEWLINE + content; | |
| writeFileSync(indexPath, content, 'utf-8'); | |
| console.log('Updated index.html'); | |
| } catch (error) { | |
| console.error('Failed to process build output:', error); | |
| } | |
| }, 100); | |
| } | |
| }; | |
| } | |