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
| // !!! Internal header, to be used by mtmd only !!! | |
| struct clip_ctx; | |
| struct clip_image_size { | |
| int width; | |
| int height; | |
| bool operator==(const clip_image_size & other) const { | |
| return width == other.width && height == other.height; | |
| } | |
| bool operator!=(const clip_image_size & other) const { | |
| return !(*this == other); | |
| } | |
| int area() const { | |
| // avoid overflow when computing area | |
| GGML_ASSERT(width >= 0 && width <= 46000); | |
| GGML_ASSERT(height >= 0 && height <= 46000); | |
| return width * height; | |
| } | |
| }; | |
| struct clip_image_f32; | |
| struct clip_image_f32_batch; | |
| enum clip_modality { | |
| CLIP_MODALITY_VISION, | |
| CLIP_MODALITY_AUDIO, | |
| }; | |
| enum clip_flash_attn_type { | |
| CLIP_FLASH_ATTN_TYPE_AUTO = -1, | |
| CLIP_FLASH_ATTN_TYPE_DISABLED = 0, | |
| CLIP_FLASH_ATTN_TYPE_ENABLED = 1, | |
| }; | |
| struct clip_context_params { | |
| bool use_gpu; | |
| enum clip_flash_attn_type flash_attn_type; | |
| int image_min_tokens; | |
| int image_max_tokens; | |
| bool warmup; | |
| ggml_backend_sched_eval_callback cb_eval; | |
| void * cb_eval_user_data; | |
| bool no_alloc; | |
| mtmd_progress_callback progress_callback; | |
| void * progress_callback_user_data; | |
| }; | |
| struct clip_init_result { | |
| struct clip_ctx * ctx_v; // vision context | |
| struct clip_ctx * ctx_a; // audio context | |
| }; | |
| struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params); | |
| void clip_free(struct clip_ctx * ctx); | |
| // TODO: should be enum, not string | |
| const char * clip_patch_merge_type(const struct clip_ctx * ctx); | |
| int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img); | |
| // for M-RoPE, this will be the number of token positions in X and Y directions | |
| // for other models, X will be the total number of tokens and Y will be 1 | |
| int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img); | |
| int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img); | |
| // this should be equal to the embedding dimension of the text model | |
| int clip_n_mmproj_embd(const struct clip_ctx * ctx); | |
| // TODO: remove clip_image_encode() and always use batched version | |
| bool clip_image_encode (struct clip_ctx * ctx, int n_threads, const clip_image_f32 * img, std::vector<float> & out_vec); | |
| bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, std::vector<float> & out_batch_embd); | |
| bool clip_is_llava(const struct clip_ctx * ctx); | |
| // note for contributor: this clip_is_(model) pattern is deprecated | |
| // do NOT add new functions like this | |
| bool clip_has_vision_encoder(const struct clip_ctx * ctx); | |
| bool clip_has_audio_encoder(const struct clip_ctx * ctx); | |
| bool clip_support_batch(const struct clip_ctx * ctx); | |
| int clip_model_n_temporal_merge(const struct clip_ctx * ctx); // TODO @ngxson : remove, refactor this | |
| std::map<ggml_backend_dev_t, size_t> clip_get_mem_usage(const struct clip_ctx * ctx); | |
| struct clip_cap { | |
| bool has_vision; | |
| bool has_audio; | |
| }; | |
| struct clip_cap clip_get_cap(const char * fname); | |