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.
- 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
- 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
- Atomic Chat
File size: 5,154 Bytes
76513ec | 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 | #pragma once
#include <string>
#include <sstream>
#include <algorithm>
#include <cstdint>
#include <cstring>
namespace jinja {
static void string_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);
}
// for displaying source code around error position
static std::string peak_source(const std::string & source, size_t pos, size_t max_peak_chars = 40) {
if (source.empty()) {
return "(no source available)";
}
std::string output;
size_t start = (pos >= max_peak_chars) ? (pos - max_peak_chars) : 0;
size_t end = std::min(pos + max_peak_chars, source.length());
std::string substr = source.substr(start, end - start);
string_replace_all(substr, "\n", "↵");
output += "..." + substr + "...\n";
std::string spaces(pos - start + 3, ' ');
output += spaces + "^";
return output;
}
static std::string fmt_error_with_source(const std::string & tag, const std::string & msg, const std::string & source, size_t pos) {
std::ostringstream oss;
oss << tag << ": " << msg << "\n";
oss << peak_source(source, pos);
return oss.str();
}
// Note: this is a simple hasher, not cryptographically secure, just for hash table usage
struct hasher {
static constexpr auto size_t_digits = sizeof(size_t) * 8;
static constexpr size_t prime = size_t_digits == 64 ? 0x100000001b3 : 0x01000193;
static constexpr size_t seed = size_t_digits == 64 ? 0xcbf29ce484222325 : 0x811c9dc5;
static constexpr auto block_size = sizeof(size_t); // in bytes; allowing the compiler to vectorize the computation
static_assert(size_t_digits == 64 || size_t_digits == 32);
static_assert(block_size == 8 || block_size == 4);
uint8_t buffer[block_size];
size_t idx = 0; // current index in buffer
size_t state = seed;
hasher() = default;
hasher(const std::type_info & type_inf) noexcept {
const auto type_hash = type_inf.hash_code();
update(&type_hash, sizeof(type_hash));
}
// Properties:
// - update is not associative: update(a).update(b) != update(b).update(a)
// - update(a ~ b) == update(a).update(b) with ~ as concatenation operator --> useful for streaming
// - update("", 0) --> state unchanged with empty input
hasher& update(void const * bytes, size_t len) noexcept {
const uint8_t * c = static_cast<uint8_t const *>(bytes);
if (len == 0) {
return *this;
}
size_t processed = 0;
// first, fill the existing buffer if it's partial
if (idx > 0) {
size_t to_fill = block_size - idx;
if (to_fill > len) {
to_fill = len;
}
std::memcpy(buffer + idx, c, to_fill);
idx += to_fill;
processed += to_fill;
if (idx == block_size) {
update_block(buffer);
idx = 0;
}
}
// process full blocks from the remaining input
for (; processed + block_size <= len; processed += block_size) {
update_block(c + processed);
}
// buffer any remaining bytes
size_t remaining = len - processed;
if (remaining > 0) {
std::memcpy(buffer, c + processed, remaining);
idx = remaining;
}
return *this;
}
// convenience function for testing only
hasher& update(const std::string & s) noexcept {
return update(s.data(), s.size());
}
// finalize and get the hash value
// note: after calling digest, the hasher state is modified, do not call update() again
size_t digest() noexcept {
// if there are remaining bytes in buffer, fill the rest with zeros and process
if (idx > 0) {
for (size_t i = idx; i < block_size; ++i) {
buffer[i] = 0;
}
update_block(buffer);
idx = 0;
}
return state;
}
private:
// IMPORTANT: block must have at least block_size bytes
void update_block(const uint8_t * block) noexcept {
size_t blk = static_cast<uint32_t>(block[0])
| (static_cast<uint32_t>(block[1]) << 8)
| (static_cast<uint32_t>(block[2]) << 16)
| (static_cast<uint32_t>(block[3]) << 24);
if constexpr (block_size == 8) {
blk = blk | (static_cast<uint64_t>(block[4]) << 32)
| (static_cast<uint64_t>(block[5]) << 40)
| (static_cast<uint64_t>(block[6]) << 48)
| (static_cast<uint64_t>(block[7]) << 56);
}
state ^= blk;
state *= prime;
}
};
} // namespace jinja
|