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: 26,702 Bytes
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#include "server-common.h"
#include <sstream>
json server_chat_convert_responses_to_chatcmpl(const json & response_body) {
if (!response_body.contains("input")) {
throw std::invalid_argument("'input' is required");
}
if (!json_value(response_body, "previous_response_id", std::string{}).empty()) {
throw std::invalid_argument("llama.cpp does not support 'previous_response_id'.");
}
const json input_value = response_body.at("input");
json chatcmpl_body = response_body;
chatcmpl_body.erase("input");
std::vector<json> chatcmpl_messages;
if (response_body.contains("instructions")) {
chatcmpl_messages.push_back({
{"role", "system"},
{"content", json_value(response_body, "instructions", std::string())},
});
chatcmpl_body.erase("instructions");
}
if (input_value.is_string()) {
// #responses_create-input-text_input
chatcmpl_messages.push_back({
{"role", "user"},
{"content", input_value},
});
} else if (input_value.is_array()) {
// #responses_create-input-input_item_list
static auto exists_and_is_array = [](const json & j, const char * key) -> bool {
return j.contains(key) && j.at(key).is_array();
};
static auto exists_and_is_string = [](const json & j, const char * key) -> bool {
return j.contains(key) && j.at(key).is_string();
};
for (json item : input_value) {
bool merge_prev = !chatcmpl_messages.empty() && chatcmpl_messages.back().value("role", "") == "assistant";
if (exists_and_is_string(item, "content")) {
// #responses_create-input-input_item_list-input_message-content-text_input
// Only "Input message" contains item["content"]::string
// After converting item["content"]::string to item["content"]::array,
// we can treat "Input message" as sum of "Item-Input message" and "Item-Output message"
item["content"] = json::array({
json {
{"text", item.at("content")},
{"type", "input_text"}
}
});
}
if (exists_and_is_array(item, "content") &&
exists_and_is_string(item, "role") &&
(item.at("role") == "user" ||
item.at("role") == "system" ||
item.at("role") == "developer")
) {
// #responses_create-input-input_item_list-item-input_message
std::vector<json> chatcmpl_content;
for (const json & input_item : item.at("content")) {
const std::string type = json_value(input_item, "type", std::string());
if (type == "input_text") {
if (!input_item.contains("text")) {
throw std::invalid_argument("'Input text' requires 'text'");
}
chatcmpl_content.push_back({
{"text", input_item.at("text")},
{"type", "text"},
});
} else if (type == "input_image") {
// While `detail` is marked as required,
// it has default value("auto") and can be omitted.
if (!input_item.contains("image_url")) {
throw std::invalid_argument("'image_url' is required");
}
chatcmpl_content.push_back({
{"image_url", json {
{"url", input_item.at("image_url")}
}},
{"type", "image_url"},
});
} else if (type == "input_file") {
throw std::invalid_argument("'input_file' is not supported by llamacpp at this moment");
} else {
throw std::invalid_argument("'type' must be one of 'input_text', 'input_image', or 'input_file'");
}
}
if (item.contains("type")) {
item.erase("type");
}
if (item.contains("status")) {
item.erase("status");
}
item["content"] = chatcmpl_content;
chatcmpl_messages.push_back(item);
} else if (exists_and_is_string(item, "role") &&
item.at("role") == "assistant" &&
exists_and_is_string(item, "type") &&
item.at("type") == "message"
) {
// #responses_create-input-input_item_list-item-output_message
auto chatcmpl_content = json::array();
// Handle both string content and array content
if (item.contains("content") && item.at("content").is_string()) {
// String content - convert to text content part
chatcmpl_content.push_back({
{"text", item.at("content")},
{"type", "text"},
});
} else if (exists_and_is_array(item, "content")) {
// Array content - process each item
for (const auto & output_text : item.at("content")) {
const std::string type = json_value(output_text, "type", std::string());
if (type == "output_text" || type == "input_text") {
// Accept both output_text and input_text (string content gets converted to input_text)
if (!exists_and_is_string(output_text, "text")) {
throw std::invalid_argument("'Output text' requires 'text'");
}
chatcmpl_content.push_back({
{"text", output_text.at("text")},
{"type", "text"},
});
} else if (type == "refusal") {
if (!exists_and_is_string(output_text, "refusal")) {
throw std::invalid_argument("'Refusal' requires 'refusal'");
}
chatcmpl_content.push_back({
{"refusal", output_text.at("refusal")},
{"type", "refusal"},
});
} else {
throw std::invalid_argument("'type' must be one of 'output_text' or 'refusal'");
}
}
}
if (merge_prev) {
auto & prev_msg = chatcmpl_messages.back();
if (!exists_and_is_array(prev_msg, "content")) {
prev_msg["content"] = json::array();
}
auto & prev_content = prev_msg["content"];
prev_content.insert(prev_content.end(), chatcmpl_content.begin(), chatcmpl_content.end());
} else {
item.erase("status");
item.erase("type");
item["content"] = chatcmpl_content;
chatcmpl_messages.push_back(item);
}
} else if (exists_and_is_string(item, "arguments") &&
exists_and_is_string(item, "call_id") &&
exists_and_is_string(item, "name") &&
exists_and_is_string(item, "type") &&
item.at("type") == "function_call"
) {
// #responses_create-input-input_item_list-item-function_tool_call
json tool_call = {
{"function", json {
{"arguments", item.at("arguments")},
{"name", item.at("name")},
}},
{"id", item.at("call_id")},
{"type", "function"},
};
if (merge_prev) {
auto & prev_msg = chatcmpl_messages.back();
if (!exists_and_is_array(prev_msg, "tool_calls")) {
prev_msg["tool_calls"] = json::array();
}
prev_msg["tool_calls"].push_back(tool_call);
} else {
chatcmpl_messages.push_back(json {
{"role", "assistant"},
{"tool_calls", json::array({tool_call})}
});
}
} else if (exists_and_is_string(item, "call_id") &&
(exists_and_is_string(item, "output") || exists_and_is_array(item, "output")) &&
exists_and_is_string(item, "type") &&
item.at("type") == "function_call_output"
) {
// #responses_create-input-input_item_list-item-function_tool_call_output
if (item.at("output").is_string()) {
chatcmpl_messages.push_back(json {
{"content", item.at("output")},
{"role", "tool"},
{"tool_call_id", item.at("call_id")},
});
} else {
json chatcmpl_outputs = item.at("output");
for (json & chatcmpl_output : chatcmpl_outputs) {
if (!chatcmpl_output.contains("type") || chatcmpl_output.at("type") != "input_text") {
throw std::invalid_argument("Output of tool call should be 'Input text'");
}
chatcmpl_output["type"] = "text";
}
chatcmpl_messages.push_back(json {
{"content", chatcmpl_outputs},
{"role", "tool"},
{"tool_call_id", item.at("call_id")},
});
}
} else if (exists_and_is_array(item, "summary") &&
exists_and_is_string(item, "type") &&
item.at("type") == "reasoning") {
// #responses_create-input-input_item_list-item-reasoning
if (!exists_and_is_array(item, "content")) {
throw std::invalid_argument("item['content'] is not an array");
}
if (item.at("content").empty()) {
throw std::invalid_argument("item['content'] is empty");
}
if (!exists_and_is_string(item.at("content")[0], "text")) {
throw std::invalid_argument("item['content']['text'] is not a string");
}
if (merge_prev) {
auto & prev_msg = chatcmpl_messages.back();
prev_msg["reasoning_content"] = item.at("content")[0].at("text");
} else {
chatcmpl_messages.push_back(json {
{"role", "assistant"},
{"content", json::array()},
{"reasoning_content", item.at("content")[0].at("text")},
});
}
} else {
throw std::invalid_argument("Cannot determine type of 'item'");
}
}
} else {
throw std::invalid_argument("'input' must be a string or array of objects");
}
chatcmpl_body["messages"] = chatcmpl_messages;
if (response_body.contains("tools")) {
if (!response_body.at("tools").is_array()) {
throw std::invalid_argument("'tools' must be an array of objects");
}
std::vector<json> chatcmpl_tools;
for (json resp_tool : response_body.at("tools")) {
json chatcmpl_tool;
const std::string type = json_value(resp_tool, "type", std::string());
if (type != "function") {
// Non-function Responses tools have no Chat Completions equivalent.
SRV_WRN("unsupported Responses tool type '%s' skipped\n", type.c_str());
continue;
}
resp_tool.erase("type");
chatcmpl_tool["type"] = "function";
if (!resp_tool.contains("strict")) {
resp_tool["strict"] = true;
}
chatcmpl_tool["function"] = resp_tool;
chatcmpl_tools.push_back(chatcmpl_tool);
}
chatcmpl_body.erase("tools");
if (!chatcmpl_tools.empty()) {
chatcmpl_body["tools"] = chatcmpl_tools;
}
}
if (response_body.contains("max_output_tokens")) {
chatcmpl_body.erase("max_output_tokens");
chatcmpl_body["max_tokens"] = response_body["max_output_tokens"];
}
return chatcmpl_body;
}
// Edits the cch section of an "x-anthropic-billing-header" system prompt.
// Does nothing to any other prompt.
//
// This is a claude message with a "cch=ef01a" attribute that breaks prefix caching.
// The cch stamp is a whitebox end-to-end integrity hint. It's not meaningful as a
// system prompt data, particularly to llama.cpp, but its presence means the prefix
// cache will not get past it: It changes on each request.
//
// Reference: https://github.com/ggml-org/llama.cpp/pull/21793
// Example header:
// ```
// x-anthropic-billing-header: cc_version=2.1.101.e51; cc_entrypoint=cli; cch=a5145;You are Claude Code, Anthropic's official CLI for Claude.
// ^^^^^
// ```
static void normalize_anthropic_billing_header(std::string & system_text) {
if (system_text.rfind("x-anthropic-billing-header:", 0) != 0) {
return;
}
const size_t header_prefix_length = strlen("x-anthropic-billing-header:");
const size_t cch_length = 5;
const size_t index_cch = system_text.find("cch=", header_prefix_length);
if (index_cch == std::string::npos) {
return;
}
const size_t index_replace = index_cch + 4;
if (index_replace + cch_length < system_text.length() && system_text[index_replace + cch_length] == ';') {
for (size_t i = 0; i < cch_length; ++i) {
system_text[index_replace + i] = 'f';
}
} else {
LOG_ERR("anthropic string not as expected: %s", system_text.c_str());
}
}
json server_chat_convert_anthropic_to_oai(const json & body) {
json oai_body;
// Convert system prompt
json oai_messages = json::array();
auto system_param = json_value(body, "system", json());
if (!system_param.is_null()) {
std::string system_content;
if (system_param.is_string()) {
system_content = system_param.get<std::string>();
normalize_anthropic_billing_header(system_content);
} else if (system_param.is_array()) {
for (const auto & block : system_param) {
if (json_value(block, "type", std::string()) == "text") {
auto system_text = json_value(block, "text", std::string());
normalize_anthropic_billing_header(system_text);
system_content += system_text;
}
}
}
oai_messages.push_back({
{"role", "system"},
{"content", system_content}
});
}
// Convert messages
if (!body.contains("messages")) {
throw std::runtime_error("'messages' is required");
}
const json & messages = body.at("messages");
if (messages.is_array()) {
for (const auto & msg : messages) {
std::string role = json_value(msg, "role", std::string());
if (!msg.contains("content")) {
if (role == "assistant") {
continue;
}
oai_messages.push_back(msg);
continue;
}
const json & content = msg.at("content");
if (content.is_string()) {
oai_messages.push_back(msg);
continue;
}
if (!content.is_array()) {
oai_messages.push_back(msg);
continue;
}
json tool_calls = json::array();
json converted_content = json::array();
json tool_results = json::array();
std::string reasoning_content;
bool has_tool_calls = false;
for (const auto & block : content) {
std::string type = json_value(block, "type", std::string());
if (type == "text") {
converted_content.push_back(block);
} else if (type == "thinking") {
reasoning_content += json_value(block, "thinking", std::string());
} else if (type == "image") {
json source = json_value(block, "source", json::object());
std::string source_type = json_value(source, "type", std::string());
if (source_type == "base64") {
std::string media_type = json_value(source, "media_type", std::string("image/jpeg"));
std::string data = json_value(source, "data", std::string());
std::ostringstream ss;
ss << "data:" << media_type << ";base64," << data;
converted_content.push_back({
{"type", "image_url"},
{"image_url", {
{"url", ss.str()}
}}
});
} else if (source_type == "url") {
std::string url = json_value(source, "url", std::string());
converted_content.push_back({
{"type", "image_url"},
{"image_url", {
{"url", url}
}}
});
}
} else if (type == "tool_use") {
tool_calls.push_back({
{"id", json_value(block, "id", std::string())},
{"type", "function"},
{"function", {
{"name", json_value(block, "name", std::string())},
{"arguments", json_value(block, "input", json::object()).dump()}
}}
});
has_tool_calls = true;
} else if (type == "tool_result") {
std::string tool_use_id = json_value(block, "tool_use_id", std::string());
auto result_content = json_value(block, "content", json());
std::string result_text;
if (result_content.is_string()) {
result_text = result_content.get<std::string>();
} else if (result_content.is_array()) {
for (const auto & c : result_content) {
if (json_value(c, "type", std::string()) == "text") {
result_text += json_value(c, "text", std::string());
}
}
}
tool_results.push_back({
{"role", "tool"},
{"tool_call_id", tool_use_id},
{"content", result_text}
});
}
}
if (!converted_content.empty() || has_tool_calls || !reasoning_content.empty()) {
json new_msg = {{"role", role}};
if (!converted_content.empty()) {
new_msg["content"] = converted_content;
} else if (has_tool_calls || !reasoning_content.empty()) {
new_msg["content"] = "";
}
if (!tool_calls.empty()) {
new_msg["tool_calls"] = tool_calls;
}
if (!reasoning_content.empty()) {
new_msg["reasoning_content"] = reasoning_content;
}
oai_messages.push_back(new_msg);
}
for (const auto & tool_msg : tool_results) {
oai_messages.push_back(tool_msg);
}
}
}
oai_body["messages"] = oai_messages;
// Convert tools
if (body.contains("tools")) {
const json & tools = body.at("tools");
if (tools.is_array()) {
json oai_tools = json::array();
for (const auto & tool : tools) {
oai_tools.push_back({
{"type", "function"},
{"function", {
{"name", json_value(tool, "name", std::string())},
{"description", json_value(tool, "description", std::string())},
{"parameters", tool.contains("input_schema") ? tool.at("input_schema") : json::object()}
}}
});
}
oai_body["tools"] = oai_tools;
}
}
// Convert tool_choice
if (body.contains("tool_choice")) {
const json & tc = body.at("tool_choice");
if (tc.is_object()) {
std::string type = json_value(tc, "type", std::string());
if (type == "auto") {
oai_body["tool_choice"] = "auto";
} else if (type == "any" || type == "tool") {
oai_body["tool_choice"] = "required";
}
}
}
// Convert stop_sequences to stop
if (body.contains("stop_sequences")) {
oai_body["stop"] = body.at("stop_sequences");
}
// Handle max_tokens (required in Anthropic, but we're permissive)
if (body.contains("max_tokens")) {
oai_body["max_tokens"] = body.at("max_tokens");
} else {
oai_body["max_tokens"] = 4096;
}
// Pass through common params
for (const auto & key : {"temperature", "top_p", "top_k", "stream", "chat_template_kwargs"}) {
if (body.contains(key)) {
oai_body[key] = body.at(key);
}
}
// Handle Anthropic-specific thinking param
if (body.contains("thinking")) {
json thinking = json_value(body, "thinking", json::object());
std::string thinking_type = json_value(thinking, "type", std::string());
if (thinking_type == "enabled") {
int budget_tokens = json_value(thinking, "budget_tokens", 10000);
oai_body["thinking_budget_tokens"] = budget_tokens;
}
}
// Handle Anthropic-specific metadata param
if (body.contains("metadata")) {
json metadata = json_value(body, "metadata", json::object());
std::string user_id = json_value(metadata, "user_id", std::string());
if (!user_id.empty()) {
oai_body["__metadata_user_id"] = user_id;
}
}
return oai_body;
}
json server_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) {
json delta = json::object();
if (!diff.reasoning_content_delta.empty()) {
delta["reasoning_content"] = diff.reasoning_content_delta;
}
if (!diff.content_delta.empty()) {
delta["content"] = diff.content_delta;
}
if (diff.tool_call_index != std::string::npos) {
json tool_call;
tool_call["index"] = diff.tool_call_index;
if (!diff.tool_call_delta.id.empty()) {
tool_call["id"] = diff.tool_call_delta.id;
tool_call["type"] = "function";
}
if (!diff.tool_call_delta.name.empty() || !diff.tool_call_delta.arguments.empty()) {
json function = json::object();
if (!diff.tool_call_delta.name.empty()) {
function["name"] = diff.tool_call_delta.name;
}
if (!diff.tool_call_delta.arguments.empty()) {
function["arguments"] = diff.tool_call_delta.arguments;
}
tool_call["function"] = function;
}
delta["tool_calls"] = json::array({ tool_call });
}
return delta;
}
json convert_transcriptions_to_chatcmpl(
const json & inp_body,
const common_chat_templates * tmpls,
const std::map<std::string, uploaded_file> & in_files,
std::vector<raw_buffer> & out_files) {
// TODO @ngxson : this function may need to be improved in the future
// handle input files
out_files.clear();
auto it = in_files.find("file");
if (it != in_files.end()) {
out_files.push_back(it->second.data);
} else {
throw std::invalid_argument("No input file found for transcription");
}
// handle input data
std::string prompt = json_value(inp_body, "prompt", std::string());
std::string language = json_value(inp_body, "language", std::string());
std::string response_format = json_value(inp_body, "response_format", std::string("json"));
if (response_format != "json") {
throw std::invalid_argument("Only 'json' response_format is supported for transcription");
}
const common_chat_prompt_preset preset = common_chat_get_asr_prompt(tmpls);
if (prompt.empty()) {
prompt = preset.user;
}
if (!language.empty()) {
prompt += string_format(" (language: %s)", language.c_str());
}
prompt += get_media_marker();
json messages = json::array();
if (!preset.system.empty()) {
messages.push_back({{"role", "system"}, {"content", preset.system}});
}
messages.push_back({{"role", "user"}, {"content", prompt}});
json chatcmpl_body = inp_body; // copy all fields
chatcmpl_body["messages"] = messages;
// because input from form-data, everything is string, we need to correct the types here
std::string stream = json_value(inp_body, "stream", std::string("false"));
chatcmpl_body["stream"] = stream == "true";
if (inp_body.contains("max_tokens")) {
std::string inp = inp_body["max_tokens"].get<std::string>();
chatcmpl_body["max_tokens"] = std::stoul(inp);
}
if (inp_body.contains("temperature")) {
std::string inp = inp_body["temperature"].get<std::string>();
chatcmpl_body["temperature"] = std::stof(inp);
}
return chatcmpl_body;
}
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