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
| namespace ov { | |
| namespace frontend { | |
| namespace ggml { | |
| class TranslateSession; | |
| typedef std::map<std::string, Output<Node>> TensorMap; | |
| class NodeContext : public frontend::NodeContext { | |
| public: | |
| NodeContext(const std::shared_ptr<GgmlDecoder> & decoder, | |
| std::shared_ptr<TensorMap> & tensor_map, | |
| int node_idx, | |
| TranslateSession * translate_session = nullptr) : | |
| ov::frontend::NodeContext(decoder->get_op_type(node_idx)), | |
| m_decoder(decoder), | |
| m_tensor_map(tensor_map), | |
| m_node_idx(node_idx), | |
| m_translate_session(translate_session) { | |
| m_input_names = decoder->get_input_names(m_node_idx); | |
| m_output_names = decoder->get_output_names(m_node_idx); | |
| } | |
| TranslateSession * get_translate_session() const { return m_translate_session; } | |
| const std::vector<std::string> & get_input_names() const { return m_input_names; } | |
| size_t get_input_size() const override { return m_decoder->get_input_size(m_node_idx); } | |
| ov::element::Type get_input_type(size_t index) const { | |
| return m_decoder->get_input_type(m_node_idx, m_input_names[index]); | |
| } | |
| PartialShape get_input_shape(size_t input_index) const { | |
| return m_decoder->get_input_shape(m_node_idx, m_input_names[input_index]); | |
| } | |
| std::vector<size_t> get_input_stride(size_t index) const { | |
| return m_decoder->get_input_stride(m_node_idx, m_input_names[index]); | |
| } | |
| std::string get_output_name() const { return m_output_names[0]; } | |
| PartialShape get_output_shape() const { return m_decoder->get_output_shape(m_node_idx); } | |
| int32_t * get_input_op_params(size_t index) const { | |
| return m_decoder->get_input_op_params(m_node_idx, m_input_names[index]); | |
| } | |
| size_t get_view_input_size(size_t index) const { | |
| return m_decoder->get_view_input_size(m_node_idx, m_input_names[index]); | |
| } | |
| size_t get_view_input_offset(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_offset(m_node_idx, m_input_names[index], view_index); | |
| } | |
| size_t get_view_input_src_offset(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_src_offset(m_node_idx, m_input_names[index], view_index); | |
| } | |
| std::vector<size_t> get_view_input_stride(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_stride(m_node_idx, m_input_names[index], view_index); | |
| } | |
| std::vector<size_t> get_view_input_src_stride(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_src_stride(m_node_idx, m_input_names[index], view_index); | |
| } | |
| ov::Shape get_view_input_ggml_shape(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_ggml_shape(m_node_idx, m_input_names[index], view_index); | |
| } | |
| ov::Shape get_view_input_src_ggml_shape(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_src_ggml_shape(m_node_idx, m_input_names[index], view_index); | |
| } | |
| ov::PartialShape get_view_input_ov_shape(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_ov_shape(m_node_idx, m_input_names[index], view_index); | |
| } | |
| ov::PartialShape get_view_input_src_ov_shape(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_src_ov_shape(m_node_idx, m_input_names[index], view_index); | |
| } | |
| std::string get_view_input_name(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_name(m_node_idx, m_input_names[index], view_index); | |
| } | |
| std::string get_view_input_src_name(size_t index, size_t view_index) const { | |
| return m_decoder->get_view_input_src_name(m_node_idx, m_input_names[index], view_index); | |
| } | |
| int32_t get_op_dynamic_dim() const { return m_decoder->get_op_dynamic_dim(m_node_idx); } | |
| int32_t * get_output_op_params() const { return m_decoder->get_output_op_params(m_node_idx); } | |
| size_t get_output_op_offset() const { return m_decoder->get_output_op_offset(m_node_idx); } | |
| ov::element::Type get_output_type() const { return m_decoder->get_output_type(m_node_idx); } | |
| std::vector<size_t> get_output_stride() const { return m_decoder->get_output_stride(m_node_idx); } | |
| Output<Node> get_input(int idx) const override { | |
| // Check if this input is a VIEW | |
| size_t view_input_size = m_decoder->get_view_input_size(m_node_idx, m_input_names[idx]); | |
| if (view_input_size > 0) { | |
| // This is a VIEW input, get the base tensor name (last element in the chain) | |
| std::string base_name = | |
| m_decoder->get_view_input_src_name(m_node_idx, m_input_names[idx], view_input_size - 1); | |
| // Check if the VIEW has been resolved (translate_view produced a Slice) | |
| auto view_it = m_tensor_map->find(m_input_names[idx]); | |
| if (!base_name.empty() && view_it != m_tensor_map->end()) { | |
| auto base_it = m_tensor_map->find(base_name); | |
| if (base_it != m_tensor_map->end() && | |
| view_it->second.get_node_shared_ptr() != base_it->second.get_node_shared_ptr()) { | |
| return view_it->second; | |
| } | |
| return base_it->second; | |
| } | |
| if (!base_name.empty()) { | |
| return m_tensor_map->at(base_name); | |
| } | |
| } | |
| // Not a VIEW or failed to get base name, use the original logic | |
| return m_tensor_map->at(m_input_names[idx]); | |
| } | |
| Output<Node> get_input(const std::string & name) const override { | |
| if (m_tensor_map->find(name) == m_tensor_map->end()) { | |
| throw std::runtime_error("'" + name + "' not found in tensor map."); | |
| } | |
| return m_tensor_map->at(name); | |
| } | |
| bool has_input(const std::string & name) const { return m_tensor_map->find(name) != m_tensor_map->end(); } | |
| const std::string & get_name() const override { return m_decoder->get_op_name(m_node_idx); } | |
| ov::Any get_attribute_as_any(const std::string & name) const override { return m_decoder->get_attribute(name); } | |
| int get_op_case() const { return m_decoder->get_op_case(m_node_idx); } | |
| bool is_static() const { return m_decoder->is_static(); } | |
| bool is_stateful() const { return m_decoder->is_stateful(); } | |
| private: | |
| std::shared_ptr<GgmlDecoder> m_decoder; | |
| std::shared_ptr<TensorMap> & m_tensor_map; | |
| int m_node_idx; | |
| TranslateSession * m_translate_session; | |
| std::vector<std::string> m_input_names; | |
| std::vector<std::string> m_output_names; | |
| }; | |
| using CreatorFunction = std::function<ov::OutputVector(const ov::frontend::ggml::NodeContext &)>; | |
| } // namespace ggml | |
| } // namespace frontend | |
| } // namespace ov | |