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 { readFileSync, writeFileSync, existsSync } from 'node:fs'; | |
| import { resolve } from 'path'; | |
| import type { Plugin } from 'vite'; | |
| import { BUILD_CONFIG } from '../src/lib/constants/pwa'; | |
| let processed = false; | |
| const OUTPUT_DIR = process.env.LLAMA_UI_OUT_DIR ?? BUILD_CONFIG.OUTPUT_DIR; | |
| function rewrite(path: string, pairs: [string, string][]): void { | |
| if (!existsSync(path)) { | |
| return; | |
| } | |
| const text = readFileSync(path, 'utf-8'); | |
| let out = text; | |
| for (const [from, to] of pairs) { | |
| out = out.split(from).join(to); | |
| } | |
| if (out !== text) { | |
| writeFileSync(path, out, 'utf-8'); | |
| } | |
| } | |
| /** | |
| * Relativize SvelteKit absolute base refs so the build is relocatable under any subpath. | |
| * | |
| * SvelteKit bakes root absolute /_app/ paths into the SPA fallback because paths.relative | |
| * does not apply to a depth agnostic fallback page. Rewriting to ./_app/ lets a plain | |
| * recursive copy of the output into /any/subdir/ resolve assets against the document URL. | |
| * Runs after adapter-static writes index.html and the PWA plugin writes sw.js, deferred the | |
| * same way as buildInfoPlugin so the emitted files exist. | |
| */ | |
| export function relativizeBasePlugin(): Plugin { | |
| return { | |
| name: 'llamacpp:relativize-base', | |
| apply: 'build', | |
| closeBundle() { | |
| setTimeout(() => { | |
| try { | |
| if (processed) return; | |
| processed = true; | |
| const outDir = resolve(OUTPUT_DIR); | |
| // index.html: modulepreload, stylesheet and bootstrap import reference "/_app/ | |
| rewrite(resolve(outDir, 'index.html'), [['"/_app/', '"./_app/']]); | |
| // sw.js: the only absolute entries are the navigate fallback precache key and handler | |
| rewrite(resolve(outDir, 'sw.js'), [ | |
| ['{url:"/"', '{url:"./"'], | |
| ['createHandlerBoundToURL("/"', 'createHandlerBoundToURL("./"'] | |
| ]); | |
| console.log('Relativized base refs in index.html and sw.js'); | |
| } catch (error) { | |
| console.error('Failed to relativize base refs:', error); | |
| } | |
| }, 100); | |
| } | |
| }; | |
| } | |