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 { browser } from '$app/environment'; | |
| import { useRegisterSW } from 'virtual:pwa-register/svelte'; | |
| import { versionStore } from '$lib/stores/version.svelte'; | |
| import { BUILD_VERSION_LOCALSTORAGE_KEY } from '$lib/constants/storage'; | |
| import { SW_CONFIG } from '$lib/constants/pwa'; | |
| /** | |
| * Hook for PWA service worker registration, update polling, and build version mismatch detection. | |
| * | |
| * Combines two concerns that always belong together: | |
| * 1. SW registration with periodic polling for updates | |
| * 2. localStorage-based version tracking for non-PWA users | |
| */ | |
| export function usePwa() { | |
| let swCheckInterval: ReturnType<typeof setInterval> | null = null; | |
| let needRefreshByStorage = $state(false); | |
| const { | |
| // offlineReady, // to do - add installation banners for iOS | |
| needRefresh: pwaNeedRefresh, | |
| updateServiceWorker | |
| } = useRegisterSW({ | |
| onRegisteredSW(swUrl: string, r: ServiceWorkerRegistration | undefined) { | |
| if (swCheckInterval) { | |
| clearInterval(swCheckInterval); | |
| } | |
| swCheckInterval = setInterval(async () => { | |
| if (!r || r.installing || !navigator?.onLine) return; | |
| try { | |
| const resp = await fetch(swUrl, { | |
| cache: SW_CONFIG.UPDATE_FETCH_OPTIONS.CACHE, | |
| headers: { | |
| cache: SW_CONFIG.UPDATE_FETCH_OPTIONS.HEADERS.CACHE, | |
| 'cache-control': SW_CONFIG.UPDATE_FETCH_OPTIONS.HEADERS.CACHE_CONTROL | |
| } | |
| }); | |
| if (resp?.status === 200) { | |
| await r.update(); | |
| } | |
| } catch (e) { | |
| console.error(e); | |
| } | |
| }, SW_CONFIG.CHECK_INTERVAL_MS); | |
| }, | |
| onRegisterError(error: unknown) { | |
| console.error('[PWA] SW registration error:', error); | |
| } | |
| }); | |
| // Detect version mismatch via localStorage. | |
| // _app/version.json is SvelteKit's native version file for PWA cache invalidation. | |
| // This comparison detects server upgrades for non-PWA users. | |
| $effect(() => { | |
| if (!browser) return; | |
| // PWA pages update via the service worker path; the storage check is the non-PWA fallback only | |
| if (navigator.serviceWorker?.controller) return; | |
| const currentVersion = versionStore.value; | |
| if (!currentVersion) return; | |
| try { | |
| const storedVersion = localStorage.getItem(BUILD_VERSION_LOCALSTORAGE_KEY); | |
| needRefreshByStorage = !!storedVersion && storedVersion !== currentVersion; | |
| localStorage.setItem(BUILD_VERSION_LOCALSTORAGE_KEY, currentVersion); | |
| } catch { | |
| needRefreshByStorage = false; | |
| } | |
| }); | |
| return { | |
| /** Writable that is true when a PWA service worker update is available */ | |
| get needRefresh() { | |
| return pwaNeedRefresh; | |
| }, | |
| updateServiceWorker, | |
| /** Version mismatch detected via localStorage (non-PWA users) */ | |
| get needRefreshByStorage() { | |
| return needRefreshByStorage; | |
| } | |
| }; | |
| } | |