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 { isValidModelName, normalizeModelName } from '$lib/utils/model-names'; | |
| describe('normalizeModelName', () => { | |
| it('preserves Hugging Face org/model format (single slash)', () => { | |
| // Single slash is treated as Hugging Face format and preserved | |
| expect(normalizeModelName('meta-llama/Llama-3.1-8B')).toBe('meta-llama/Llama-3.1-8B'); | |
| expect(normalizeModelName('models/model-name-1')).toBe('models/model-name-1'); | |
| }); | |
| it('extracts filename from multi-segment paths', () => { | |
| // Multiple slashes -> extract just the filename | |
| expect(normalizeModelName('path/to/model/model-name-2')).toBe('model-name-2'); | |
| expect(normalizeModelName('/absolute/path/to/model')).toBe('model'); | |
| }); | |
| it('extracts filename from backslash paths', () => { | |
| expect(normalizeModelName('C\\Models\\model-name-1')).toBe('model-name-1'); | |
| expect(normalizeModelName('path\\to\\model\\model-name-2')).toBe('model-name-2'); | |
| }); | |
| it('handles mixed path separators', () => { | |
| expect(normalizeModelName('path/to\\model/model-name-2')).toBe('model-name-2'); | |
| }); | |
| it('returns simple names as-is', () => { | |
| expect(normalizeModelName('simple-model')).toBe('simple-model'); | |
| expect(normalizeModelName('model-name-2')).toBe('model-name-2'); | |
| }); | |
| it('trims whitespace', () => { | |
| expect(normalizeModelName(' model-name ')).toBe('model-name'); | |
| }); | |
| it('returns empty string for empty input', () => { | |
| expect(normalizeModelName('')).toBe(''); | |
| expect(normalizeModelName(' ')).toBe(''); | |
| }); | |
| }); | |
| describe('isValidModelName', () => { | |
| it('returns true for valid names', () => { | |
| expect(isValidModelName('model')).toBe(true); | |
| expect(isValidModelName('path/to/model.bin')).toBe(true); | |
| }); | |
| it('returns false for empty values', () => { | |
| expect(isValidModelName('')).toBe(false); | |
| expect(isValidModelName(' ')).toBe(false); | |
| }); | |
| }); | |