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
| /// <reference types="@vitest/browser/matchers" /> | |
| /// <reference types="@vitest/browser/providers/playwright" /> | |
| import { beforeEach, vi } from 'vitest'; | |
| // Mock fetch for API calls during client tests. | |
| // In test environment there is no backend server, so we intercept | |
| // the specific endpoints the app uses and return valid mock data. | |
| beforeEach(() => { | |
| const originalFetch = globalThis.fetch; | |
| vi.spyOn(globalThis, 'fetch').mockImplementation( | |
| async (input: RequestInfo | URL, init?: RequestInit) => { | |
| const url = typeof input === 'string' ? input : input instanceof URL ? input.href : input.url; | |
| // Mock server props endpoint | |
| if (url.includes('/server')) { | |
| return new Response( | |
| JSON.stringify({ | |
| mode: 'router', | |
| version: 'test', | |
| git_commit: 'test', | |
| git_branch: 'test' | |
| }), | |
| { status: 200, headers: { 'Content-Type': 'application/json' } } | |
| ); | |
| } | |
| // Mock models list endpoint | |
| if (/\/v1\/models|\/models\b/.test(url)) { | |
| return new Response( | |
| JSON.stringify({ | |
| object: 'list', | |
| data: [ | |
| { | |
| id: 'test-model.gguf', | |
| object: 'model', | |
| owned_by: 'llamacpp', | |
| created: 0, | |
| in_cache: false, | |
| path: 'models/test-model.gguf', | |
| status: { value: 'unloaded' }, | |
| meta: {} | |
| } | |
| ], | |
| models: [ | |
| { | |
| model: 'test-model.gguf', | |
| name: 'Test Model', | |
| details: {} | |
| } | |
| ] | |
| }), | |
| { status: 200, headers: { 'Content-Type': 'application/json' } } | |
| ); | |
| } | |
| // Mock /props endpoint (used for modalities) | |
| if (url.includes('/props')) { | |
| return new Response( | |
| JSON.stringify({ | |
| default_generation_settings: { n_ctx: 2048 } | |
| }), | |
| { status: 200, headers: { 'Content-Type': 'application/json' } } | |
| ); | |
| } | |
| // Mock /tools endpoint (used for built-in tools list) | |
| if (url.includes('/tools')) { | |
| return new Response(JSON.stringify([]), { | |
| status: 200, | |
| headers: { 'Content-Type': 'application/json' } | |
| }); | |
| } | |
| // Default: use real fetch | |
| return originalFetch(input, init); | |
| } | |
| ); | |
| }); | |