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 module lang="ts"> | |
| import { defineMeta } from '@storybook/addon-svelte-csf'; | |
| import ChatScreenForm from '$lib/components/app/chat/ChatScreen/ChatScreenForm.svelte'; | |
| import { expect } from 'storybook/test'; | |
| import jpgAsset from './fixtures/assets/1.jpg?url'; | |
| import svgAsset from './fixtures/assets/hf-logo.svg?url'; | |
| import pdfAsset from './fixtures/assets/example.pdf?raw'; | |
| const { Story } = defineMeta({ | |
| title: 'Components/ChatScreen/ChatScreenForm', | |
| component: ChatScreenForm, | |
| parameters: { | |
| layout: 'centered' | |
| } | |
| }); | |
| let fileAttachments = $state([ | |
| { | |
| id: '1', | |
| name: '1.jpg', | |
| type: 'image/jpeg', | |
| size: 44891, | |
| preview: jpgAsset, | |
| file: new File([''], '1.jpg', { type: 'image/jpeg' }) | |
| }, | |
| { | |
| id: '2', | |
| name: 'hf-logo.svg', | |
| type: 'image/svg+xml', | |
| size: 1234, | |
| preview: svgAsset, | |
| file: new File([''], 'hf-logo.svg', { type: 'image/svg+xml' }) | |
| }, | |
| { | |
| id: '3', | |
| name: 'example.pdf', | |
| type: 'application/pdf', | |
| size: 351048, | |
| file: new File([pdfAsset], 'example.pdf', { type: 'application/pdf' }) | |
| } | |
| ]); | |
| </script> | |
| <Story | |
| name="Default" | |
| args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }} | |
| play={async ({ canvas, userEvent }) => { | |
| const textarea = await canvas.findByRole('textbox'); | |
| const submitButton = await canvas.findByRole('button', { name: 'Send' }); | |
| // Expect the input to be focused after the component is mounted | |
| await expect(textarea).toHaveFocus(); | |
| // Expect the submit button to be disabled | |
| await expect(submitButton).toBeDisabled(); | |
| const text = 'What is the meaning of life?'; | |
| await userEvent.clear(textarea); | |
| await userEvent.type(textarea, text); | |
| await expect(textarea).toHaveValue(text); | |
| const fileInput = document.querySelector('input[type="file"]'); | |
| await expect(fileInput).not.toHaveAttribute('accept'); | |
| }} | |
| /> | |
| <Story name="Loading" args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]', isLoading: true }} /> | |
| <Story | |
| name="FileAttachments" | |
| args={{ | |
| class: 'max-w-[56rem] w-[calc(100vw-2rem)]', | |
| uploadedFiles: fileAttachments | |
| }} | |
| play={async ({ canvas }) => { | |
| const jpgAttachment = canvas.getByAltText('1.jpg'); | |
| const svgAttachment = canvas.getByAltText('hf-logo.svg'); | |
| const pdfFileExtension = canvas.getByText('PDF'); | |
| const pdfAttachment = canvas.getByText('example.pdf'); | |
| const pdfSize = canvas.getByText('342.82 KB'); | |
| await expect(jpgAttachment).toBeInTheDocument(); | |
| await expect(jpgAttachment).toHaveAttribute('src', jpgAsset); | |
| await expect(svgAttachment).toBeInTheDocument(); | |
| await expect(svgAttachment).toHaveAttribute('src', svgAsset); | |
| await expect(pdfFileExtension).toBeInTheDocument(); | |
| await expect(pdfAttachment).toBeInTheDocument(); | |
| await expect(pdfSize).toBeInTheDocument(); | |
| }} | |
| /> | |