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: 9,059 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | #include "arg.h"
#include "common.h"
#include "download.h"
#include <string>
#include <vector>
#include <sstream>
#include <unordered_set>
#undef NDEBUG
#include <cassert>
static void test(void) {
common_params params;
printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
try {
auto ctx_arg = common_params_parser_init(params, (enum llama_example)ex);
common_params_add_preset_options(ctx_arg.options);
std::unordered_set<std::string> seen_args;
std::unordered_set<std::string> seen_env_vars;
for (const auto & opt : ctx_arg.options) {
// check for args duplications
for (const auto & arg : opt.get_args()) {
if (seen_args.find(arg) == seen_args.end()) {
seen_args.insert(arg);
} else {
fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg.c_str());
exit(1);
}
}
// check for env var duplications
for (const auto & env : opt.get_env()) {
if (seen_env_vars.find(env) == seen_env_vars.end()) {
seen_env_vars.insert(env);
} else {
fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", env.c_str());
exit(1);
}
}
// exclude spec args from this check
// ref: https://github.com/ggml-org/llama.cpp/pull/22397
const bool skip = opt.is_spec;
// ensure shorter argument precedes longer argument
if (!skip && opt.args.size() > 1) {
const std::string first(opt.args.front());
const std::string last(opt.args.back());
if (first.length() > last.length()) {
fprintf(stderr, "test-arg-parser: shorter argument should come before longer one: %s, %s\n",
first.c_str(), last.c_str());
assert(false);
}
}
// same check for negated arguments
if (opt.args_neg.size() > 1) {
const std::string first(opt.args_neg.front());
const std::string last(opt.args_neg.back());
if (first.length() > last.length()) {
fprintf(stderr, "test-arg-parser: shorter negated argument should come before longer one: %s, %s\n",
first.c_str(), last.c_str());
assert(false);
}
}
}
} catch (std::exception & e) {
printf("%s\n", e.what());
assert(false);
}
}
auto list_str_to_char = [](std::vector<std::string> & argv) -> std::vector<char *> {
std::vector<char *> res;
for (auto & arg : argv) {
res.push_back(const_cast<char *>(arg.data()));
}
return res;
};
std::vector<std::string> argv;
printf("test-arg-parser: test invalid usage\n\n");
// missing value
argv = {"binary_name", "-m"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// wrong value (int)
argv = {"binary_name", "-ngl", "hello"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// wrong value (enum)
argv = {"binary_name", "-sm", "hello"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// non-existence arg in specific example (--draft cannot be used outside llama-speculative)
argv = {"binary_name", "--draft", "123"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING));
// negated arg
argv = {"binary_name", "--no-mmap"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
printf("test-arg-parser: test valid usage\n\n");
argv = {"binary_name", "-m", "model_file.gguf"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model.path == "model_file.gguf");
argv = {"binary_name", "-t", "1234"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.cpuparams.n_threads == 1234);
argv = {"binary_name", "--verbose"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.verbosity > 1);
argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model.path == "abc.gguf");
assert(params.n_predict == 6789);
assert(params.n_batch == 9090);
// --draft cannot be used outside llama-speculative
argv = {"binary_name", "--spec-draft-n-max", "123"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
assert(params.speculative.draft.n_max == 123);
// multi-value args (CSV)
argv = {"binary_name", "--lora", "file1.gguf,\"file2,2.gguf\",\"file3\"\"3\"\".gguf\",file4\".gguf"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.lora_adapters.size() == 4);
assert(params.lora_adapters[0].path == "file1.gguf");
assert(params.lora_adapters[1].path == "file2,2.gguf");
assert(params.lora_adapters[2].path == "file3\"3\".gguf");
assert(params.lora_adapters[3].path == "file4\".gguf");
// skip this part on windows, because setenv is not supported
#ifdef _WIN32
printf("test-arg-parser: skip on windows build\n");
#else
printf("test-arg-parser: test environment variables (valid + invalid usages)\n\n");
setenv("LLAMA_ARG_THREADS", "blah", true);
argv = {"binary_name"};
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model.path == "blah.gguf");
assert(params.cpuparams.n_threads == 1010);
printf("test-arg-parser: test negated environment variables\n\n");
setenv("LLAMA_ARG_MMAP", "0", true);
setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format
argv = {"binary_name"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.use_mmap == false);
assert(params.no_perf == true);
printf("test-arg-parser: test environment variables being overwritten\n\n");
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name", "-m", "overwritten.gguf"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model.path == "overwritten.gguf");
assert(params.cpuparams.n_threads == 1010);
#endif // _WIN32
printf("test-arg-parser: test download functions\n\n");
const char * GOOD_URL = "http://ggml.ai/";
const char * BAD_URL = "http://ggml.ai/404";
{
printf("test-arg-parser: test good URL\n\n");
auto res = common_remote_get_content(GOOD_URL, {});
assert(res.first == 200);
assert(res.second.size() > 0);
std::string str(res.second.data(), res.second.size());
assert(str.find("llama.cpp") != std::string::npos);
}
{
printf("test-arg-parser: test bad URL\n\n");
auto res = common_remote_get_content(BAD_URL, {});
assert(res.first == 404);
}
{
printf("test-arg-parser: test max size error\n");
common_remote_params params;
params.max_size = 1;
try {
common_remote_get_content(GOOD_URL, params);
assert(false && "it should throw an error");
} catch (std::exception & e) {
printf(" expected error: %s\n\n", e.what());
}
}
printf("test-arg-parser: all tests OK\n\n");
}
int main(void) {
try {
test();
} catch (std::exception & e) {
fprintf(stderr, "test-arg-parser: exception: %s\n", e.what());
return 1;
}
return 0;
}
|