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
libmtmd dev guide
History
Please refer to multimodal.md for a broader context.
In short:
libmtmdstarted as a wrapper aroundlibllava/clip.cpp- Various components that used to be in
clip.cppare moved progressively to mtmd. For example, preprocessor is now part of mtmd
Terminologies
- mtmd: MulTiMoDal
- bitmap: representing a raw input data, for example: RGB image, PCM audio
- tiles / slices: for llava-uhd-style models, the preprocessor breaks a large input into smaller square images called tiles or slices
- chunk: a mtmd_input_chunk represents a preprocessed input that can then be passed through
mtmd_encode()
Pipeline
A typical pipeline of the core libmtmd is as follows:
- A bitmap (RGB image or PCM audio) is created
- Bitmap and the text prompt is provided to
mtmd_tokenize()that breaks the input into chunks- The tokenizer function first expands a "lazy" bitmap if it finds one. Typically, this is used by video, so that one media token corresponds to one input bitmap
- For models that support "fused" temporal frames like Qwen-VL, the tokenizer tries to merge pair of consecutive frames into one batch
- The preprocessor will then be called, which produces a list of chunks
- Depending on the model itself, special tokens will be injected to separate image chunks (i.e. llava-uhd-style models)
- Multiple bitmaps may be batched together to form a larger
mtmd_batch() - Single image or batch is encoded, via
mtmd_encode()ormtmd_batch_encode() - Get the output embeddings
Helper
We provide a set of helper functions via mtmd_helper to make using libmtmd easier. The helper provides:
- Image, audio and video file decoding (for example, decode raw JPEG into RGB bitmap)
- Manage
llama_batchand calls tollama_decode