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 { expect, test } from '@playwright/test'; | |
| test.describe('PWA Service Worker', () => { | |
| test('service worker is registered', async ({ page }) => { | |
| await page.goto('/'); | |
| const swURL = await page.evaluate(async () => { | |
| const registration = await Promise.race([ | |
| // eslint-disable-next-line @typescript-eslint/ban-ts-comment | |
| // @ts-ignore - type inference differs from browser runtime | |
| navigator.serviceWorker.ready, | |
| new Promise((_, reject) => | |
| setTimeout(() => reject(new Error('Service worker registration failed: timeout')), 15000) | |
| ) | |
| ]); | |
| // @ts-expect-error registration is of type unknown | |
| return registration.active?.scriptURL; | |
| }); | |
| expect(swURL).toBeTruthy(); | |
| expect(swURL).toContain('/sw.js'); | |
| }); | |
| test('service worker has precache configured', async ({ page }) => { | |
| await page.goto('/'); | |
| await page.evaluate(async () => { | |
| await navigator.serviceWorker.ready; | |
| }); | |
| const swActive = await page.evaluate(async () => { | |
| const reg = await navigator.serviceWorker.ready; | |
| return reg.active?.scriptURL ?? null; | |
| }); | |
| expect(swActive).toBeTruthy(); | |
| const swResponse = await page.request.get(swActive!); | |
| const swContent = await swResponse.text(); | |
| // Precache contains SvelteKit content-hashed bundle paths | |
| expect(swContent).toMatch(/"_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/); | |
| expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/); | |
| expect(swContent).toMatch(/"manifest\.webmanifest"/); | |
| expect(swContent).toMatch(/"_app\/version\.json"/); | |
| // NavigationRoute is intentionally absent — server API endpoints | |
| // (e.g. /slots, /models) must not be intercepted by the PWA and | |
| // should return JSON directly from the server. | |
| expect(swContent).not.toMatch(/NavigationRoute/); | |
| expect(swContent).toMatch(/api-cache/); | |
| }); | |
| test('offline mode - page loads when offline after caching', async ({ browser }) => { | |
| const context = await browser.newContext(); | |
| const offlinePage = await context.newPage(); | |
| await offlinePage.goto('/'); | |
| await offlinePage.waitForLoadState('networkidle'); | |
| await offlinePage.evaluate(async () => { | |
| await navigator.serviceWorker.ready; | |
| }); | |
| await offlinePage.waitForTimeout(2000); | |
| await context.setOffline(true); | |
| await offlinePage.goto('/'); | |
| const bodyText = await offlinePage.locator('body').textContent(); | |
| expect(bodyText).toBeTruthy(); | |
| await context.close(); | |
| }); | |
| test('version.json is accessible and contains version', async ({ page }) => { | |
| const versionResponse = await page.request.get('/_app/version.json'); | |
| expect(versionResponse.ok()).toBeTruthy(); | |
| const versionData = await versionResponse.json(); | |
| expect(versionData).toHaveProperty('version'); | |
| expect(typeof versionData.version).toBe('string'); | |
| expect(versionData.version.length).toBeGreaterThan(0); | |
| }); | |
| test('manifest.webmanifest is accessible and valid', async ({ page }) => { | |
| const response = await page.request.get('/manifest.webmanifest'); | |
| expect(response.ok()).toBeTruthy(); | |
| const manifest = await response.json(); | |
| expect(manifest).toHaveProperty('name', 'llama-ui'); | |
| expect(manifest).toHaveProperty('short_name', 'llama-ui'); | |
| expect(manifest).toHaveProperty('start_url', './'); | |
| expect(manifest).toHaveProperty('display', 'standalone'); | |
| expect(manifest.icons).toBeTruthy(); | |
| expect(manifest.icons.length).toBeGreaterThan(0); | |
| }); | |
| test('index.html contains content-hashed bundle references', async ({ page }) => { | |
| const response = await page.request.get('/'); | |
| expect(response.ok()).toBeTruthy(); | |
| const html = await response.text(); | |
| // SvelteKit outputs content-hashed bundle names in _app/immutable/ | |
| expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/); | |
| expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/); | |
| expect(html).toMatch(/import\("(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"\)/); | |
| }); | |
| }); | |