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
File size: 20,521 Bytes
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#include "server-http.h"
#include "server-models.h"
#include "server-cors-proxy.h"
#include "server-stream.h"
#include "server-tools.h"
#include "arg.h"
#include "build-info.h"
#include "common.h"
#include "fit.h"
#include "llama.h"
#include "log.h"
#include <atomic>
#include <clocale>
#include <exception>
#include <signal.h>
#include <thread> // for std::thread::hardware_concurrency
#if defined(_WIN32)
#include <windows.h>
#endif
static std::function<void(int)> shutdown_handler;
static std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
static inline void signal_handler(int signal) {
if (is_terminating.test_and_set()) {
// in case it hangs, we can force terminate the server by hitting Ctrl+C twice
// this is for better developer experience, we can remove when the server is stable enough
fprintf(stderr, "Received second interrupt, terminating immediately.\n");
exit(1);
}
shutdown_handler(signal);
}
// wrapper function that handles exceptions and logs errors
// this is to make sure handler_t never throws exceptions; instead, it returns an error response
static server_http_context::handler_t ex_wrapper(server_http_context::handler_t func) {
return [func = std::move(func)](const server_http_req & req) -> server_http_res_ptr {
std::string message;
error_type error;
try {
return func(req);
} catch (const std::invalid_argument & e) {
// treat invalid_argument as invalid request (400)
error = ERROR_TYPE_INVALID_REQUEST;
message = e.what();
} catch (const std::exception & e) {
// treat other exceptions as server error (500)
error = ERROR_TYPE_SERVER;
message = e.what();
} catch (...) {
error = ERROR_TYPE_SERVER;
message = "unknown error";
}
auto res = std::make_unique<server_http_res>();
res->status = 500;
try {
json error_data = format_error_response(message, error);
res->status = json_value(error_data, "code", 500);
res->data = safe_json_to_str({{ "error", error_data }});
SRV_WRN("got exception: %s\n", res->data.c_str());
} catch (const std::exception & e) {
SRV_ERR("got another exception: %s | while handling exception: %s\n", e.what(), message.c_str());
res->data = "Internal Server Error";
}
return res;
};
}
// satisfies -Wmissing-declarations
int llama_server(int argc, char ** argv);
int llama_server(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
// own arguments required by this example
common_params params;
common_init();
// start the stream session manager GC right after common init, before any HTTP route can
// touch it. lifecycle is symmetric, stop_gc() runs in clean_up() before backend free
g_stream_sessions.start_gc();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
return 1;
}
llama_backend_init();
llama_numa_init(params.numa);
common_models_handler models_handler;
try {
models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
if (common_models_handler_is_preset_repo(models_handler)) {
// apply the preset and start the server in router mode
common_models_handler_apply(models_handler, params);
}
} catch (const std::exception & e) {
SRV_ERR("failed to fetch model metadata: %s\n", e.what());
return 1;
}
// router server never loads a model and must not touch the GPU
const bool is_router_server = params.model.path.empty()
&& params.model.hf_repo.empty();
// skip device enumeration so the CUDA primary context stays uncreated
common_params_print_info(params, !is_router_server);
if (!is_router_server) {
// validate batch size for embeddings
// embeddings require all tokens to be processed in a single ubatch
// see https://github.com/ggml-org/llama.cpp/issues/12836
if (params.embedding && params.n_batch > params.n_ubatch) {
SRV_WRN("embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", params.n_batch, params.n_ubatch);
SRV_WRN("setting n_batch = n_ubatch = %d to avoid assertion failure\n", params.n_ubatch);
params.n_batch = params.n_ubatch;
}
if (params.n_parallel < 0) {
SRV_TRC("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
params.n_parallel = 4;
params.kv_unified = true;
}
}
// for consistency between server router mode and single-model mode, we set the same model name as alias
auto model_name = params.model.get_name();
if (params.model_alias.empty() && !model_name.empty()) {
params.model_alias.insert(model_name);
}
// struct that contains llama context and inference
server_context ctx_server;
server_http_context ctx_http;
if (!ctx_http.init(params)) {
SRV_ERR("%s", "failed to initialize HTTP server\n");
return 1;
}
//
// Router
//
// register API routes
server_child child; // only used in non-router mode
server_routes routes(params, ctx_server);
server_tools tools;
std::optional<server_models_routes> models_routes{};
if (is_router_server) {
// setup server instances manager
try {
models_routes.emplace(params, argc, argv);
} catch (const std::exception & e) {
SRV_ERR("failed to initialize router models: %s\n", e.what());
return 1;
}
// proxy handlers
// note: routes.get_health stays the same
routes.get_metrics = models_routes->proxy_get;
routes.post_props = models_routes->proxy_post;
routes.post_completions = models_routes->proxy_post;
routes.post_completions_oai = models_routes->proxy_post;
routes.post_chat_completions = models_routes->proxy_post;
routes.post_control = models_routes->proxy_post;
routes.post_responses_oai = models_routes->proxy_post;
routes.post_transcriptions_oai = models_routes->proxy_post;
routes.post_anthropic_messages = models_routes->proxy_post;
routes.post_anthropic_count_tokens = models_routes->proxy_post;
routes.post_infill = models_routes->proxy_post;
routes.post_embeddings = models_routes->proxy_post;
routes.post_embeddings_oai = models_routes->proxy_post;
routes.post_rerank = models_routes->proxy_post;
routes.post_tokenize = models_routes->proxy_post;
routes.post_detokenize = models_routes->proxy_post;
routes.post_apply_template = models_routes->proxy_post;
routes.post_chat_completions_tok = models_routes->proxy_post;
routes.post_responses_tok_oai = models_routes->proxy_post;
routes.get_lora_adapters = models_routes->proxy_get;
routes.post_lora_adapters = models_routes->proxy_post;
routes.get_slots = models_routes->proxy_get;
routes.post_slots = models_routes->proxy_post;
// custom routes for router
routes.get_props = models_routes->get_router_props;
routes.get_models = models_routes->get_router_models;
ctx_http.post("/models", ex_wrapper(models_routes->post_router_models));
ctx_http.post("/models/load", ex_wrapper(models_routes->post_router_models_load));
ctx_http.post("/models/unload", ex_wrapper(models_routes->post_router_models_unload));
ctx_http.get ("/models/sse", ex_wrapper(models_routes->get_router_models_sse));
ctx_http.del ("/models", ex_wrapper(models_routes->del_router_models));
}
ctx_http.get ("/health", ex_wrapper(routes.get_health)); // public endpoint (no API key check)
ctx_http.get ("/v1/health", ex_wrapper(routes.get_health)); // public endpoint (no API key check)
ctx_http.get ("/metrics", ex_wrapper(routes.get_metrics));
ctx_http.get ("/props", ex_wrapper(routes.get_props));
ctx_http.post("/props", ex_wrapper(routes.post_props));
ctx_http.get ("/models", ex_wrapper(routes.get_models)); // public endpoint (no API key check)
ctx_http.get ("/v1/models", ex_wrapper(routes.get_models)); // public endpoint (no API key check)
ctx_http.post("/completion", ex_wrapper(routes.post_completions)); // legacy
ctx_http.post("/completions", ex_wrapper(routes.post_completions));
ctx_http.post("/v1/completions", ex_wrapper(routes.post_completions_oai));
ctx_http.post("/chat/completions", ex_wrapper(routes.post_chat_completions));
ctx_http.post("/v1/chat/completions", ex_wrapper(routes.post_chat_completions));
ctx_http.post("/v1/chat/completions/control", ex_wrapper(routes.post_control));
ctx_http.post("/v1/responses", ex_wrapper(routes.post_responses_oai));
ctx_http.post("/responses", ex_wrapper(routes.post_responses_oai));
ctx_http.post("/v1/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai));
ctx_http.post("/audio/transcriptions", ex_wrapper(routes.post_transcriptions_oai));
ctx_http.post("/v1/messages", ex_wrapper(routes.post_anthropic_messages)); // anthropic messages API
ctx_http.post("/infill", ex_wrapper(routes.post_infill));
ctx_http.post("/embedding", ex_wrapper(routes.post_embeddings)); // legacy
ctx_http.post("/embeddings", ex_wrapper(routes.post_embeddings));
ctx_http.post("/v1/embeddings", ex_wrapper(routes.post_embeddings_oai));
ctx_http.post("/rerank", ex_wrapper(routes.post_rerank));
ctx_http.post("/reranking", ex_wrapper(routes.post_rerank));
ctx_http.post("/v1/rerank", ex_wrapper(routes.post_rerank));
ctx_http.post("/v1/reranking", ex_wrapper(routes.post_rerank));
ctx_http.post("/tokenize", ex_wrapper(routes.post_tokenize));
ctx_http.post("/detokenize", ex_wrapper(routes.post_detokenize));
ctx_http.post("/apply-template", ex_wrapper(routes.post_apply_template));
// token counting
ctx_http.post("/chat/completions/input_tokens", ex_wrapper(routes.post_chat_completions_tok));
ctx_http.post("/v1/chat/completions/input_tokens", ex_wrapper(routes.post_chat_completions_tok));
ctx_http.post("/responses/input_tokens", ex_wrapper(routes.post_responses_tok_oai));
ctx_http.post("/v1/responses/input_tokens", ex_wrapper(routes.post_responses_tok_oai));
ctx_http.post("/v1/messages/count_tokens", ex_wrapper(routes.post_anthropic_count_tokens)); // anthropic token counting
// LoRA adapters hotswap
ctx_http.get ("/lora-adapters", ex_wrapper(routes.get_lora_adapters));
ctx_http.post("/lora-adapters", ex_wrapper(routes.post_lora_adapters));
// Save & load slots
ctx_http.get ("/slots", ex_wrapper(routes.get_slots));
ctx_http.post("/slots/:id_slot", ex_wrapper(routes.post_slots));
// resumable streaming, the conversation_id is the session identity end to end. router and
// child wire different handlers under the same paths: a child binds the local g_stream_sessions
// backed factories, the router binds proxies that resolve the owning child through the
// conv_id -> model map
server_http_context::handler_t stream_get_h;
server_http_context::handler_t streams_lookup_h;
server_http_context::handler_t stream_delete_h;
if (is_router_server) {
stream_get_h = models_routes->router_stream_get;
streams_lookup_h = models_routes->router_streams_lookup;
stream_delete_h = models_routes->router_stream_delete;
} else {
stream_get_h = make_stream_get_handler();
streams_lookup_h = make_streams_lookup_handler();
stream_delete_h = make_stream_delete_handler();
}
ctx_http.get ("/v1/stream/:conv_id", ex_wrapper(stream_get_h));
// POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids
// you already own (the WebUI passes the convs visible in its sidebar). the server never
// lists ids it has not been asked about, so a random caller cannot enumerate live sessions
ctx_http.post("/v1/streams/lookup", ex_wrapper(streams_lookup_h));
ctx_http.del ("/v1/stream/:conv_id", ex_wrapper(stream_delete_h));
// Google Cloud Platform (Vertex AI) compat
ctx_http.register_gcp_compat();
// return 403 for disabled features
server_http_context::handler_t res_403 = [](const server_http_req &) {
auto res = std::make_unique<server_http_res>();
res->status = 403;
res->data = safe_json_to_str({
{"error", {
{"message", "this feature is disabled"},
{"type", "feature_disabled"},
}}
});
return res;
};
// CORS proxy (EXPERIMENTAL, only used by the Web UI for MCP)
if (params.ui_mcp_proxy) {
SRV_WRN("%s", "-----------------\n");
SRV_WRN("%s", "CORS proxy is enabled, do not expose server to untrusted environments\n");
SRV_WRN("%s", "This feature is EXPERIMENTAL and may be removed or changed in future versions\n");
SRV_WRN("%s", "-----------------\n");
ctx_http.get ("/cors-proxy", ex_wrapper(proxy_handler_get));
ctx_http.post("/cors-proxy", ex_wrapper(proxy_handler_post));
} else {
ctx_http.get ("/cors-proxy", ex_wrapper(res_403));
ctx_http.post("/cors-proxy", ex_wrapper(res_403));
}
// EXPERIMENTAL built-in tools
if (!params.server_tools.empty()) {
try {
tools.setup(params.server_tools);
} catch (const std::exception & e) {
SRV_ERR("tools setup failed: %s\n", e.what());
return 1;
}
SRV_WRN("%s", "-----------------\n");
SRV_WRN("%s", "Built-in tools are enabled, do not expose server to untrusted environments\n");
SRV_WRN("%s", "This feature is EXPERIMENTAL and may be changed in the future\n");
SRV_WRN("%s", "-----------------\n");
ctx_http.get ("/tools", ex_wrapper(tools.handle_get));
ctx_http.post("/tools", ex_wrapper(tools.handle_post));
} else {
ctx_http.get ("/tools", ex_wrapper(res_403));
ctx_http.post("/tools", ex_wrapper(res_403));
}
//
// Handle downloading model
//
if (child.is_child() && child.get_mode() == SERVER_CHILD_MODE_DOWNLOAD) {
return child.run_download(params);
} else if (!is_router_server) {
// single-model mode (NOT spawned by router)
try {
common_models_handler_apply(models_handler, params);
} catch (const std::exception & e) {
SRV_ERR("failed to download model: %s\n", e.what());
return 1;
}
}
//
// Start the server
//
std::function<void()> clean_up;
if (is_router_server) {
SRV_INF("%s", "starting server in router mode. models will be automatically loaded on-demand\n");
clean_up = [&models_routes]() {
SRV_INF("%s: cleaning up before exit...\n", __func__);
// stop the session GC first, it finalizes live sessions and wakes pending readers
g_stream_sessions.stop_gc();
if (models_routes.has_value()) {
models_routes->stopping.store(true); // maybe redundant, but just to be safe
models_routes->models.unload_all();
}
llama_backend_free();
};
if (!ctx_http.start()) {
clean_up();
SRV_ERR("%s", "exiting due to HTTP server error\n");
return 1;
}
ctx_http.is_ready.store(true);
shutdown_handler = [&](int) {
if (models_routes.has_value()) {
// important to disconnect any SSE clients
models_routes->stopping.store(true);
}
ctx_http.stop();
};
} else {
// setup clean up function, to be called before exit
clean_up = [&ctx_http, &ctx_server]() {
SRV_INF("%s: cleaning up before exit...\n", __func__);
// stop the session GC first, it finalizes live sessions and wakes pending readers
g_stream_sessions.stop_gc();
ctx_http.stop();
ctx_server.terminate();
llama_backend_free();
};
// start the HTTP server before loading the model to be able to serve /health requests
if (!ctx_http.start()) {
clean_up();
SRV_ERR("%s", "exiting due to HTTP server error\n");
return 1;
}
// setup communication child --> router if necessary
if (child.is_child()) {
ctx_server.set_state_callback([&](server_state state, json payload) {
child.notify_to_router(server_state_to_str(state), payload);
});
}
if (!ctx_server.load_model(params)) {
clean_up();
if (ctx_http.thread.joinable()) {
ctx_http.thread.join();
}
SRV_ERR("%s", "exiting due to model loading error\n");
return 1;
}
routes.update_meta(ctx_server);
ctx_http.is_ready.store(true);
SRV_INF("%s", "model loaded\n");
shutdown_handler = [&](int) {
// this will unblock start_loop()
ctx_server.terminate();
};
}
// TODO: refactor in common/console
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = signal_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
sigaction(SIGTERM, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
SRV_INF("listening on %s\n", ctx_http.listening_address.c_str());
if (is_router_server) {
SRV_WRN("%s", "NOTE: router mode is experimental\n");
SRV_WRN("%s", " it is not recommended to use this mode in untrusted environments\n");
if (!params.models_preset_hf.empty()) {
SRV_WRN( "NOTE: using preset.ini from HF repo '%s'\n", params.models_preset_hf.c_str());
SRV_WRN("%s", " please only use presets that you can trust! Unknown presets may be unsafe\n");
}
if (ctx_http.thread.joinable()) {
ctx_http.thread.join(); // keep the main thread alive
}
// when the HTTP server stops, clean up and exit
clean_up();
} else {
// optionally, notify router server that this instance is ready
std::thread monitor_thread;
if (child.is_child()) {
monitor_thread = child.setup(shutdown_handler);
child.notify_to_router(server_state_to_str(SERVER_STATE_READY), routes.get_model_info());
}
// this call blocks the main thread until queue_tasks.terminate() is called
ctx_server.start_loop();
clean_up();
if (ctx_http.thread.joinable()) {
ctx_http.thread.join();
}
if (monitor_thread.joinable()) {
monitor_thread.join();
}
auto * ll_ctx = ctx_server.get_llama_context();
if (ll_ctx != nullptr) {
common_memory_breakdown_print(ll_ctx);
}
}
return 0;
}
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