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
| struct mtmd_audio_mel { | |
| int64_t n_len; | |
| int64_t n_len_org; | |
| int64_t n_mel; | |
| std::vector<float> data; | |
| }; | |
| struct mtmd_audio_mel_filters { | |
| int64_t n_mel; | |
| int64_t n_fft; | |
| std::vector<float> data; | |
| }; | |
| // cache for audio processing, each processor instance owns its own cache | |
| struct mtmd_audio_cache { | |
| std::vector<float> sin_vals; | |
| std::vector<float> cos_vals; | |
| std::vector<float> hann_window; | |
| mtmd_audio_mel_filters filters; | |
| void fill_sin_cos_table(uint32_t n); | |
| void fill_hann_window(uint32_t length, bool periodic); | |
| // Build mel filterbank matrix [n_mel × n_fft_bins] at runtime. | |
| // n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257. | |
| void fill_mel_filterbank_matrix(int64_t n_mel, | |
| int64_t n_fft, | |
| int sample_rate, // e.g. 16000 | |
| float fmin = 0.0f, // e.g. 0.0 | |
| float fmax = -1.0f, // e.g. sr/2; pass -1 for auto | |
| bool slaney_area_norm = true, | |
| float scale = 1.0f, | |
| bool use_htk = false | |
| ); | |
| }; | |
| struct mtmd_audio_preprocessor { | |
| const clip_hparams & hparams; | |
| mtmd_audio_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {} | |
| virtual ~mtmd_audio_preprocessor() = default; | |
| virtual void initialize() = 0; // NOT thread-safe | |
| virtual bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) = 0; | |
| }; | |
| struct mtmd_audio_preprocessor_whisper : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_whisper(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| private: | |
| mtmd_audio_cache cache; | |
| }; | |
| struct mtmd_audio_preprocessor_conformer : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_conformer(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| private: | |
| mtmd_audio_cache cache; | |
| }; | |
| struct mtmd_audio_preprocessor_granite_speech : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_granite_speech(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| private: | |
| mtmd_audio_cache cache; | |
| }; | |
| struct mtmd_audio_preprocessor_gemma4a : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_gemma4a(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| private: | |
| mtmd_audio_cache cache; | |
| }; | |
| struct mtmd_audio_preprocessor_gemma4ua : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_gemma4ua(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| }; | |
| struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor { | |
| mtmd_audio_preprocessor_qwen3a(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} | |
| void initialize() override; | |
| bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override; | |
| private: | |
| mtmd_audio_cache cache; | |
| }; | |
| // | |
| // streaming ISTFT - converts spectrogram frames back to audio one frame at a time | |
| // | |
| struct mtmd_audio_streaming_istft { | |
| mtmd_audio_streaming_istft(int n_fft, int hop_length); | |
| // reset streaming state | |
| void reset(); | |
| // process a single STFT frame (streaming) | |
| // frame_spectrum: [n_fft_bins x 2] interleaved real/imag | |
| // returns: up to hop_length samples | |
| std::vector<float> process_frame(const float * frame_spectrum); | |
| // flush remaining samples at end of stream | |
| std::vector<float> flush(); | |
| private: | |
| int n_fft; | |
| int hop_length; | |
| int n_fft_bins; | |
| // Own cache for output processing | |
| mtmd_audio_cache cache; | |
| // Streaming state | |
| std::vector<float> overlap_buffer; | |
| std::vector<float> window_sum_buffer; | |
| int padding_to_remove; | |
| // Working buffers for IFFT | |
| std::vector<float> ifft_in; | |
| std::vector<float> ifft_out; | |
| }; | |