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 llama_logger_state { | |
| ggml_log_callback log_callback = llama_log_callback_default; | |
| void * log_callback_user_data = nullptr; | |
| }; | |
| static llama_logger_state g_logger_state; | |
| time_meas::time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {} | |
| time_meas::~time_meas() { | |
| if (t_start_us >= 0) { | |
| t_acc += ggml_time_us() - t_start_us; | |
| } | |
| } | |
| void llama_log_get(ggml_log_callback * log_callback, void ** user_data) { | |
| ggml_log_get(log_callback, user_data); | |
| } | |
| void llama_log_set(ggml_log_callback log_callback, void * user_data) { | |
| ggml_log_set(log_callback, user_data); | |
| g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default; | |
| g_logger_state.log_callback_user_data = user_data; | |
| } | |
| static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) { | |
| va_list args_copy; | |
| va_copy(args_copy, args); | |
| char buffer[128]; | |
| int len = vsnprintf(buffer, 128, format, args); | |
| if (len < 128) { | |
| g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data); | |
| } else { | |
| char * buffer2 = new char[len + 1]; | |
| vsnprintf(buffer2, len + 1, format, args_copy); | |
| buffer2[len] = 0; | |
| g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data); | |
| delete[] buffer2; | |
| } | |
| va_end(args_copy); | |
| } | |
| void llama_log_internal(ggml_log_level level, const char * format, ...) { | |
| va_list args; | |
| va_start(args, format); | |
| llama_log_internal_v(level, format, args); | |
| va_end(args); | |
| } | |
| void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) { | |
| (void) level; | |
| (void) user_data; | |
| fputs(text, stderr); | |
| fflush(stderr); | |
| } | |
| void replace_all(std::string & s, const std::string & search, const std::string & replace) { | |
| if (search.empty()) { | |
| return; | |
| } | |
| std::string builder; | |
| builder.reserve(s.length()); | |
| size_t pos = 0; | |
| size_t last_pos = 0; | |
| while ((pos = s.find(search, last_pos)) != std::string::npos) { | |
| builder.append(s, last_pos, pos - last_pos); | |
| builder.append(replace); | |
| last_pos = pos + search.length(); | |
| } | |
| builder.append(s, last_pos, std::string::npos); | |
| s = std::move(builder); | |
| } | |
| std::string format(const char * fmt, ...) { | |
| va_list ap; | |
| va_list ap2; | |
| va_start(ap, fmt); | |
| va_copy(ap2, ap); | |
| int size = vsnprintf(NULL, 0, fmt, ap); | |
| GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT | |
| std::vector<char> buf(size + 1); | |
| int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); | |
| GGML_ASSERT(size2 == size); | |
| va_end(ap2); | |
| va_end(ap); | |
| return std::string(buf.data(), size); | |
| } | |
| std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) { | |
| char buf[256]; | |
| snprintf(buf, sizeof(buf), "%6" PRId64, ne.at(0)); | |
| for (size_t i = 1; i < ne.size(); i++) { | |
| snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %6" PRId64, ne.at(i)); | |
| } | |
| return buf; | |
| } | |
| std::string llama_format_tensor_shape(const struct ggml_tensor * t) { | |
| char buf[256]; | |
| snprintf(buf, sizeof(buf), "%6" PRId64, t->ne[0]); | |
| for (int i = 1; i < GGML_MAX_DIMS; i++) { | |
| snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %6" PRId64, t->ne[i]); | |
| } | |
| return buf; | |
| } | |
| static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { | |
| switch (type) { | |
| case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]); | |
| case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]); | |
| case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]); | |
| case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]); | |
| case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]); | |
| case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]); | |
| case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]); | |
| case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]); | |
| case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]); | |
| case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]); | |
| case GGUF_TYPE_BOOL: return ((const int8_t *)data)[i] != 0 ? "true" : "false"; | |
| default: return format("unknown type %d", type); | |
| } | |
| } | |
| std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { | |
| const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); | |
| switch (type) { | |
| case GGUF_TYPE_STRING: | |
| return gguf_get_val_str(ctx_gguf, i); | |
| case GGUF_TYPE_ARRAY: | |
| { | |
| const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); | |
| int arr_n = gguf_get_arr_n(ctx_gguf, i); | |
| const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); | |
| std::stringstream ss; | |
| ss << "["; | |
| for (int j = 0; j < arr_n; j++) { | |
| if (arr_type == GGUF_TYPE_STRING) { | |
| std::string val = gguf_get_arr_str(ctx_gguf, i, j); | |
| // escape quotes | |
| replace_all(val, "\\", "\\\\"); | |
| replace_all(val, "\"", "\\\""); | |
| ss << '"' << val << '"'; | |
| } else if (arr_type == GGUF_TYPE_ARRAY) { | |
| ss << "???"; | |
| } else { | |
| ss << gguf_data_to_str(arr_type, data, j); | |
| } | |
| if (j < arr_n - 1) { | |
| ss << ", "; | |
| } | |
| } | |
| ss << "]"; | |
| return ss.str(); | |
| } | |
| default: | |
| return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0); | |
| } | |
| } | |