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
Debugging Tests Tips
How to run & execute or debug a specific test without anything else to keep the feedback loop short?
There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
For example, running the following command will output an interactive list from which you can select a test. It takes this form:
debug-test.sh [OPTION]... <test_regex> <test_number>
It will then build & run in the debugger for you.
To just execute a test and get back a PASS or FAIL message run:
./scripts/debug-test.sh test-tokenizer
To test in GDB use the -g flag to enable gdb test mode.
./scripts/debug-test.sh -g test-tokenizer
# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
>>> b main
To speed up the testing loop, if you know your test number you can just run it similar to below:
./scripts/debug-test.sh test 23
For further reference use debug-test.sh -h to print help.
How does the script work?
If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
Step 1: Reset and Setup folder context
From base of this repository, let's create build-ci-debug as our build context.
rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
Step 2: Setup Build Environment and Compile Test Binaries
Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
make -j
Step 3: Find all tests available that matches REGEX
The output of this command will give you the command & arguments needed to run GDB.
-R test-tokenizer: looks for all the test files namedtest-tokenizer*(R=Regex)-N: "show-only" disables test execution & shows test commands that you can feed to GDB.-V: Verbose Mode
ctest -R "test-tokenizer" -V -N
This may return output similar to below (focusing on key lines to pay attention to):
...
1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
1: Working Directory: .
Labels: main
Test #1: test-tokenizer-0-llama-spm
...
4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
4: Working Directory: .
Labels: main
Test #4: test-tokenizer-0-falcon
...
Step 4: Identify Test Command for Debugging
So for test #1 above we can tell these two pieces of relevant information:
- Test Binary:
~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 - Test GGUF Model:
~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf
Step 5: Run GDB on test command
Based on the ctest 'test command' report above we can then run a gdb session via this command below:
gdb --args ${Test Binary} ${Test GGUF Model}
Example:
gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"