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
| <script lang="ts"> | |
| import { DialogModelNotAvailable } from '$lib/components/app'; | |
| import { chatStore } from '$lib/stores/chat.svelte'; | |
| import { conversationsStore, isConversationsInitialized } from '$lib/stores/conversations.svelte'; | |
| import { modelsStore, modelOptions } from '$lib/stores/models.svelte'; | |
| import { onMount } from 'svelte'; | |
| import { page } from '$app/state'; | |
| import { replaceState } from '$app/navigation'; | |
| import { APP_NAME, NEW_CHAT_PARAM } from '$lib/constants'; | |
| let qParam = $derived(page.url.searchParams.get('q')); | |
| let modelParam = $derived(page.url.searchParams.get('model')); | |
| let newChatParam = $derived(page.url.searchParams.get(NEW_CHAT_PARAM)); | |
| // Dialog state for model not available error | |
| let showModelNotAvailable = $state(false); | |
| let requestedModelName = $state(''); | |
| let availableModelNames = $derived(modelOptions().map((m) => m.model)); | |
| /** | |
| * Clear URL params after message is sent to prevent re-sending on refresh | |
| */ | |
| function clearUrlParams() { | |
| const url = new URL(page.url); | |
| url.searchParams.delete('q'); | |
| url.searchParams.delete('model'); | |
| url.searchParams.delete(NEW_CHAT_PARAM); | |
| replaceState(url.toString(), {}); | |
| } | |
| async function handleUrlParams() { | |
| await modelsStore.fetch(); | |
| if (modelParam) { | |
| const model = modelsStore.findModelByName(modelParam); | |
| if (model) { | |
| try { | |
| await modelsStore.selectModelById(model.id); | |
| } catch (error) { | |
| console.error('Failed to select model:', error); | |
| requestedModelName = modelParam; | |
| showModelNotAvailable = true; | |
| return; | |
| } | |
| } else { | |
| requestedModelName = modelParam; | |
| showModelNotAvailable = true; | |
| return; | |
| } | |
| } | |
| // Handle ?q= parameter - create new conversation and send message | |
| if (qParam !== null) { | |
| await conversationsStore.createConversation(); | |
| clearUrlParams(); | |
| } else if (modelParam || newChatParam === 'true') { | |
| clearUrlParams(); | |
| } | |
| } | |
| onMount(async () => { | |
| if (!isConversationsInitialized()) { | |
| await conversationsStore.initialize(); | |
| } | |
| conversationsStore.clearActiveConversation(); | |
| chatStore.clearUIState(); | |
| await modelsStore.fetch(); | |
| if (qParam !== null || modelParam !== null || newChatParam === 'true') { | |
| await handleUrlParams(); | |
| } | |
| await modelsStore.ensureFirstModelSelected(); | |
| }); | |
| </script> | |
| <svelte:head> | |
| <title>{APP_NAME}</title> | |
| </svelte:head> | |
| <DialogModelNotAvailable | |
| bind:open={showModelNotAvailable} | |
| modelName={requestedModelName} | |
| availableModels={availableModelNames} | |
| /> | |