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
| /** | |
| * CORS Proxy utility for routing requests through llama-server's CORS proxy. | |
| */ | |
| import { base } from '$app/paths'; | |
| import { | |
| CORS_PROXY_ENDPOINT, | |
| CORS_PROXY_HEADER_PREFIX, | |
| CORS_PROXY_URL_PARAM | |
| } from '$lib/constants'; | |
| /** | |
| * Build a proxied URL that routes through llama-server's CORS proxy. | |
| * @param targetUrl - The original URL to proxy | |
| * @returns URL pointing to the CORS proxy with target encoded | |
| */ | |
| export function buildProxiedUrl(targetUrl: string): URL { | |
| const proxyPath = `${base}${CORS_PROXY_ENDPOINT}`; | |
| const proxyUrl = new URL(proxyPath, window.location.origin); | |
| proxyUrl.searchParams.set(CORS_PROXY_URL_PARAM, targetUrl); | |
| return proxyUrl; | |
| } | |
| /** | |
| * Wrap original headers for proxying through the CORS proxy. This avoids issues with duplicated llama.cpp-specific and target headers when using the CORS proxy. | |
| * @param headers - The original headers to be proxied to target | |
| * @returns List of "wrapped" headers to be sent to the CORS proxy | |
| */ | |
| export function buildProxiedHeaders(headers: Record<string, string>): Record<string, string> { | |
| const proxiedHeaders: Record<string, string> = {}; | |
| for (const [key, value] of Object.entries(headers)) { | |
| proxiedHeaders[`${CORS_PROXY_HEADER_PREFIX}${key}`] = value; | |
| } | |
| return proxiedHeaders; | |
| } | |