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 { afterEach, beforeAll, beforeEach, describe, expect, it } from 'vitest'; | |
| // node env unit project has no DOM, install a minimal localStorage backed by a Map | |
| beforeAll(() => { | |
| const store = new Map<string, string>(); | |
| const polyfill: Storage = { | |
| get length() { | |
| return store.size; | |
| }, | |
| clear: () => store.clear(), | |
| getItem: (k) => (store.has(k) ? store.get(k)! : null), | |
| key: (i) => Array.from(store.keys())[i] ?? null, | |
| removeItem: (k) => { | |
| store.delete(k); | |
| }, | |
| setItem: (k, v) => { | |
| store.set(k, String(v)); | |
| } | |
| }; | |
| (globalThis as unknown as { localStorage: Storage }).localStorage = polyfill; | |
| }); | |
| import { ChatService } from '$lib/services/chat.service'; | |
| import { STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX } from '$lib/constants'; | |
| describe('ChatService stream resume', () => { | |
| beforeEach(() => { | |
| localStorage.clear(); | |
| }); | |
| afterEach(() => { | |
| localStorage.clear(); | |
| }); | |
| it('returns null when no state exists for the conversation', () => { | |
| expect(ChatService.getStreamState('conv-a')).toBeNull(); | |
| }); | |
| it('saves and reads back the byte count', () => { | |
| ChatService.saveStreamState('conv-a', 4242); | |
| const got = ChatService.getStreamState('conv-a'); | |
| expect(got).not.toBeNull(); | |
| expect(got!.bytesReceived).toBe(4242); | |
| expect(typeof got!.updatedAt).toBe('number'); | |
| }); | |
| it('overwrites the previous byte count on a new save for the same conversation', () => { | |
| ChatService.saveStreamState('conv-a', 100); | |
| ChatService.saveStreamState('conv-a', 200); | |
| const got = ChatService.getStreamState('conv-a'); | |
| expect(got!.bytesReceived).toBe(200); | |
| }); | |
| it('keeps states for distinct conversations isolated', () => { | |
| ChatService.saveStreamState('conv-a', 10); | |
| ChatService.saveStreamState('conv-b', 20); | |
| expect(ChatService.getStreamState('conv-a')!.bytesReceived).toBe(10); | |
| expect(ChatService.getStreamState('conv-b')!.bytesReceived).toBe(20); | |
| }); | |
| it('clears the state for a given conversation', () => { | |
| ChatService.saveStreamState('conv-a', 10); | |
| ChatService.clearStreamState('conv-a'); | |
| expect(ChatService.getStreamState('conv-a')).toBeNull(); | |
| }); | |
| it('ignores empty conversation id on save', () => { | |
| ChatService.saveStreamState('', 1); | |
| expect(ChatService.getStreamState('')).toBeNull(); | |
| }); | |
| it('returns null on corrupted storage payload', () => { | |
| localStorage.setItem(`${STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX}conv-a`, '{not-json'); | |
| expect(ChatService.getStreamState('conv-a')).toBeNull(); | |
| }); | |
| it('persists the model alongside the byte count', () => { | |
| ChatService.saveStreamState('conv-a', 10, 'model-x'); | |
| expect(ChatService.getStreamState('conv-a')!.model).toBe('model-x'); | |
| }); | |
| it('stores a null model when none is provided', () => { | |
| ChatService.saveStreamState('conv-a', 10); | |
| expect(ChatService.getStreamState('conv-a')!.model).toBeNull(); | |
| }); | |
| it('overwrites the model on a new save for the same conversation', () => { | |
| ChatService.saveStreamState('conv-a', 10, 'model-x'); | |
| ChatService.saveStreamState('conv-a', 20, 'model-y'); | |
| expect(ChatService.getStreamState('conv-a')!.model).toBe('model-y'); | |
| }); | |
| describe('resumeStreamIdentity', () => { | |
| it('appends the persisted model so the resume key matches the frozen POST identity', () => { | |
| ChatService.saveStreamState('conv-a', 10, 'model-x'); | |
| expect( | |
| ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown') | |
| ).toBe('conv-a::model-x'); | |
| }); | |
| it('keeps the bare conv id when the persisted model is null', () => { | |
| ChatService.saveStreamState('conv-a', 10); | |
| expect( | |
| ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown') | |
| ).toBe('conv-a'); | |
| }); | |
| it('falls back to the current model only when no state is persisted', () => { | |
| expect(ChatService.resumeStreamIdentity('conv-a', null, 'dropdown')).toBe('conv-a::dropdown'); | |
| }); | |
| it('ignores the fallback when a state exists, the persisted value is authoritative', () => { | |
| ChatService.saveStreamState('conv-a', 10, 'model-x'); | |
| expect( | |
| ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown') | |
| ).toBe('conv-a::model-x'); | |
| }); | |
| it('falls back when a legacy state has no model field', () => { | |
| localStorage.setItem( | |
| `${STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX}conv-a`, | |
| JSON.stringify({ bytesReceived: 10, updatedAt: 1 }) | |
| ); | |
| expect( | |
| ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown') | |
| ).toBe('conv-a::dropdown'); | |
| }); | |
| }); | |
| }); | |