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 { SvelteMap } from 'svelte/reactivity'; | |
| import type { ModelOption } from '$lib/types/models'; | |
| export interface ModelItem { | |
| option: ModelOption; | |
| flatIndex: number; | |
| } | |
| export interface OrgGroup { | |
| orgName: string | null; | |
| items: ModelItem[]; | |
| } | |
| export interface GroupedModelOptions { | |
| loaded: ModelItem[]; | |
| favorites: ModelItem[]; | |
| available: OrgGroup[]; | |
| } | |
| export function filterModelOptions(options: ModelOption[], searchTerm: string): ModelOption[] { | |
| const term = searchTerm.trim().toLowerCase(); | |
| if (!term) return options; | |
| return options.filter( | |
| (option) => | |
| option.model.toLowerCase().includes(term) || | |
| option.name?.toLowerCase().includes(term) || | |
| option.aliases?.some((alias: string) => alias.toLowerCase().includes(term)) || | |
| option.tags?.some((tag: string) => tag.toLowerCase().includes(term)) | |
| ); | |
| } | |
| export function groupModelOptions( | |
| filteredOptions: ModelOption[], | |
| favoriteIds: Set<string>, | |
| isModelLoaded: (model: string) => boolean | |
| ): GroupedModelOptions { | |
| // Loaded models | |
| const loaded: ModelItem[] = []; | |
| for (let i = 0; i < filteredOptions.length; i++) { | |
| if (isModelLoaded(filteredOptions[i].model)) { | |
| loaded.push({ option: filteredOptions[i], flatIndex: i }); | |
| } | |
| } | |
| // Favorites (excluding loaded) | |
| const loadedModelIds = new Set(loaded.map((item) => item.option.model)); | |
| const favorites: ModelItem[] = []; | |
| for (let i = 0; i < filteredOptions.length; i++) { | |
| if ( | |
| favoriteIds.has(filteredOptions[i].model) && | |
| !loadedModelIds.has(filteredOptions[i].model) | |
| ) { | |
| favorites.push({ option: filteredOptions[i], flatIndex: i }); | |
| } | |
| } | |
| // Available models grouped by org (excluding loaded and favorites) | |
| const available: OrgGroup[] = []; | |
| const orgGroups = new SvelteMap<string, ModelItem[]>(); | |
| for (let i = 0; i < filteredOptions.length; i++) { | |
| const option = filteredOptions[i]; | |
| if (loadedModelIds.has(option.model) || favoriteIds.has(option.model)) continue; | |
| const key = option.parsedId?.orgName ?? ''; | |
| if (!orgGroups.has(key)) orgGroups.set(key, []); | |
| orgGroups.get(key)!.push({ option, flatIndex: i }); | |
| } | |
| for (const [orgName, items] of orgGroups) { | |
| available.push({ orgName: orgName || null, items }); | |
| } | |
| return { loaded, favorites, available }; | |
| } | |