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 { describe, expect, it } from 'vitest'; | |
| import { ChatService } from '$lib/services/chat.service'; | |
| import type { ApiStreamSession } from '$lib/types'; | |
| function makeSession(overrides: Partial<ApiStreamSession>): ApiStreamSession { | |
| return { | |
| conversation_id: 'conv', | |
| is_done: true, | |
| total_bytes: 0, | |
| started_at: 0, | |
| completed_at: 0, | |
| ...overrides | |
| }; | |
| } | |
| describe('selectActiveStream', () => { | |
| it('returns null on empty input', () => { | |
| expect(ChatService.selectActiveStream([])).toBeNull(); | |
| }); | |
| it('returns null on null or undefined input', () => { | |
| expect(ChatService.selectActiveStream(null)).toBeNull(); | |
| expect(ChatService.selectActiveStream(undefined)).toBeNull(); | |
| }); | |
| it('returns the single session when it is running', () => { | |
| const s = makeSession({ conversation_id: 'only', is_done: false, started_at: 42 }); | |
| expect(ChatService.selectActiveStream([s])).toBe(s); | |
| }); | |
| it('returns null when the single session is finalized', () => { | |
| const s = makeSession({ conversation_id: 'only', is_done: true, started_at: 42 }); | |
| expect(ChatService.selectActiveStream([s])).toBeNull(); | |
| }); | |
| it('prefers a still running session over a finalized one regardless of started_at', () => { | |
| const finalized = makeSession({ conversation_id: 'old', is_done: true, started_at: 1000 }); | |
| const running = makeSession({ conversation_id: 'new', is_done: false, started_at: 10 }); | |
| expect(ChatService.selectActiveStream([finalized, running])?.conversation_id).toBe('new'); | |
| expect(ChatService.selectActiveStream([running, finalized])?.conversation_id).toBe('new'); | |
| }); | |
| it('among running sessions, picks the most recently started one', () => { | |
| const a = makeSession({ conversation_id: 'a', is_done: false, started_at: 100 }); | |
| const b = makeSession({ conversation_id: 'b', is_done: false, started_at: 200 }); | |
| const c = makeSession({ conversation_id: 'c', is_done: false, started_at: 150 }); | |
| expect(ChatService.selectActiveStream([a, b, c])?.conversation_id).toBe('b'); | |
| expect(ChatService.selectActiveStream([c, a, b])?.conversation_id).toBe('b'); | |
| }); | |
| it('returns null when all sessions are finalized, the DB already holds the content', () => { | |
| const a = makeSession({ conversation_id: 'a', is_done: true, started_at: 10 }); | |
| const b = makeSession({ conversation_id: 'b', is_done: true, started_at: 30 }); | |
| const c = makeSession({ conversation_id: 'c', is_done: true, started_at: 20 }); | |
| expect(ChatService.selectActiveStream([a, b, c])).toBeNull(); | |
| }); | |
| it('keeps the first match on ties when both are running with identical started_at', () => { | |
| // reduce visits left to right, the initial accumulator stays unless a strictly greater value appears | |
| const a = makeSession({ conversation_id: 'first', is_done: false, started_at: 50 }); | |
| const b = makeSession({ conversation_id: 'second', is_done: false, started_at: 50 }); | |
| expect(ChatService.selectActiveStream([a, b])?.conversation_id).toBe('first'); | |
| }); | |
| it('handles a typical realistic mix: two finalized old, one freshly running, one freshly finalized', () => { | |
| const old1 = makeSession({ conversation_id: 'old1', is_done: true, started_at: 100 }); | |
| const old2 = makeSession({ conversation_id: 'old2', is_done: true, started_at: 200 }); | |
| const freshFin = makeSession({ conversation_id: 'freshFin', is_done: true, started_at: 500 }); | |
| const running = makeSession({ conversation_id: 'running', is_done: false, started_at: 400 }); | |
| expect(ChatService.selectActiveStream([old1, old2, freshFin, running])?.conversation_id).toBe( | |
| 'running' | |
| ); | |
| }); | |
| }); | |