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
| /** | |
| * Active model resolution and capability detection for the ChatScreen. | |
| * | |
| * Picks the model that should be used for the current view | |
| * (router: user-selected or conversation fallback; non-router: first | |
| * available option), and reactively tracks which modalities (vision / | |
| * audio / video) it supports — fetching model props from the server on | |
| * demand if they aren't cached yet. | |
| */ | |
| import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte'; | |
| import { isRouterMode } from '$lib/stores/server.svelte'; | |
| import { chatStore } from '$lib/stores/chat.svelte'; | |
| import { activeMessages } from '$lib/stores/conversations.svelte'; | |
| export function useChatScreenActiveModel() { | |
| const isRouter = $derived(isRouterMode()); | |
| const conversationModel = $derived( | |
| chatStore.getConversationModel(activeMessages() as DatabaseMessage[]) | |
| ); | |
| const activeModelId = $derived.by(() => { | |
| const options = modelOptions(); | |
| if (!isRouter) { | |
| return options.length > 0 ? options[0].model : null; | |
| } | |
| const selectedId = selectedModelId(); | |
| if (selectedId) { | |
| const model = options.find((m) => m.id === selectedId); | |
| if (model) return model.model; | |
| } | |
| if (conversationModel) { | |
| const model = options.find((m) => m.model === conversationModel); | |
| if (model) return model.model; | |
| } | |
| return null; | |
| }); | |
| let modelPropsVersion = $state(0); | |
| $effect(() => { | |
| if (activeModelId) { | |
| const cached = modelsStore.getModelProps(activeModelId); | |
| if (!cached) { | |
| modelsStore.fetchModelProps(activeModelId).then(() => { | |
| modelPropsVersion++; | |
| }); | |
| } | |
| } | |
| }); | |
| const hasAudioModality = $derived.by(() => { | |
| if (activeModelId) { | |
| void modelPropsVersion; | |
| return modelsStore.modelSupportsAudio(activeModelId); | |
| } | |
| return false; | |
| }); | |
| const hasVideoModality = $derived.by(() => { | |
| if (activeModelId) { | |
| void modelPropsVersion; | |
| return modelsStore.modelSupportsVideo(activeModelId); | |
| } | |
| return false; | |
| }); | |
| const hasVisionModality = $derived.by(() => { | |
| if (activeModelId) { | |
| void modelPropsVersion; | |
| return modelsStore.modelSupportsVision(activeModelId); | |
| } | |
| return false; | |
| }); | |
| return { | |
| get isRouter() { | |
| return isRouter; | |
| }, | |
| get conversationModel() { | |
| return conversationModel; | |
| }, | |
| get activeModelId() { | |
| return activeModelId; | |
| }, | |
| get hasAudioModality() { | |
| return hasAudioModality; | |
| }, | |
| get hasVideoModality() { | |
| return hasVideoModality; | |
| }, | |
| get hasVisionModality() { | |
| return hasVisionModality; | |
| } | |
| }; | |
| } | |