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: 9,535 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | #include "reasoning-budget.h"
#include "common.h"
#include "unicode.h"
#include "log.h"
#include <cmath>
#include <cstdint>
#include <string>
#include <vector>
struct token_matcher {
std::vector<llama_token> tokens;
size_t pos = 0;
bool advance(llama_token token) {
if (tokens.empty()) {
return false;
}
if (token == tokens[pos]) {
pos++;
if (pos >= tokens.size()) {
pos = 0;
return true;
}
} else {
pos = 0;
if (token == tokens[0]) {
pos = 1;
}
}
return false;
}
void reset() { pos = 0; }
};
struct common_reasoning_budget_ctx {
const llama_vocab * vocab;
token_matcher start_matcher;
token_matcher end_matcher;
std::vector<llama_token> forced_tokens;
int32_t budget; // maximum tokens in reasoning block
int32_t remaining; // tokens remaining in budget
common_reasoning_budget_state state;
// for forcing
size_t force_pos; // next position in forced_tokens to force
};
static const char * common_reasoning_budget_name(const struct llama_sampler * /*smpl*/) {
return "reasoning-budget";
}
static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_token token) {
auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
switch (ctx->state) {
case REASONING_BUDGET_IDLE:
{
if (ctx->start_matcher.advance(token)) {
ctx->state = REASONING_BUDGET_COUNTING;
ctx->remaining = ctx->budget;
COM_TRC("activated, budget=%d tokens\n", ctx->budget);
if (ctx->remaining <= 0) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
COM_TRC("%s", "budget=0, forcing immediately\n");
}
}
break;
}
case REASONING_BUDGET_COUNTING:
case REASONING_BUDGET_WAITING_UTF8:
{
if (ctx->end_matcher.advance(token)) {
ctx->state = REASONING_BUDGET_DONE;
COM_TRC("%s", "deactivated (natural end)\n");
break;
}
bool utf8_complete = true;
if (ctx->vocab != nullptr) {
const std::string piece = common_token_to_piece(ctx->vocab, token, false);
utf8_complete = common_utf8_is_complete(piece);
}
if (ctx->state == REASONING_BUDGET_WAITING_UTF8) {
if (utf8_complete) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
COM_TRC("%s", "UTF-8 complete, now forcing end sequence\n");
}
} else if (ctx->state == REASONING_BUDGET_COUNTING) {
ctx->remaining--;
if (ctx->remaining <= 0) {
if (utf8_complete) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
COM_TRC("%s", "budget exhausted, forcing end sequence\n");
} else {
ctx->state = REASONING_BUDGET_WAITING_UTF8;
ctx->end_matcher.reset();
COM_TRC("%s", "budget exhausted, waiting for UTF-8 completion\n");
}
}
}
break;
}
case REASONING_BUDGET_FORCING:
ctx->force_pos++;
if (ctx->force_pos >= ctx->forced_tokens.size()) {
ctx->state = REASONING_BUDGET_DONE;
COM_TRC("%s", "forced sequence complete, done\n");
}
break;
case REASONING_BUDGET_DONE:
// Re-arm on a new start tag: some models emit multiple <think> blocks
// per response, and each should get a fresh budget window.
if (ctx->start_matcher.advance(token)) {
ctx->state = REASONING_BUDGET_COUNTING;
ctx->remaining = ctx->budget;
ctx->end_matcher.reset();
COM_TRC("re-activated on new start tag, budget=%d tokens\n", ctx->budget);
if (ctx->remaining <= 0) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
COM_TRC("%s", "budget=0, forcing immediately\n");
}
}
break;
}
}
static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
if (ctx->state != REASONING_BUDGET_FORCING) {
// passthrough — don't modify logits
return;
}
if (ctx->force_pos >= ctx->forced_tokens.size()) {
return;
}
const llama_token forced = ctx->forced_tokens[ctx->force_pos];
// set all logits to -inf except the forced token
for (size_t i = 0; i < cur_p->size; i++) {
if (cur_p->data[i].id != forced) {
cur_p->data[i].logit = -INFINITY;
}
}
}
static void common_reasoning_budget_reset(struct llama_sampler * smpl) {
auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
ctx->state = REASONING_BUDGET_IDLE;
ctx->remaining = ctx->budget;
ctx->start_matcher.reset();
ctx->end_matcher.reset();
ctx->force_pos = 0;
}
static struct llama_sampler * common_reasoning_budget_init_state(
const struct llama_vocab * vocab, const std::vector<llama_token> & start_tokens,
const std::vector<llama_token> & end_tokens, const std::vector<llama_token> & forced_tokens,
int32_t budget, common_reasoning_budget_state initial_state);
static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl);
static void common_reasoning_budget_free(struct llama_sampler * smpl) {
delete (common_reasoning_budget_ctx *) smpl->ctx;
}
static struct llama_sampler_i common_reasoning_budget_i = {
/* .name = */ common_reasoning_budget_name,
/* .accept = */ common_reasoning_budget_accept,
/* .apply = */ common_reasoning_budget_apply,
/* .reset = */ common_reasoning_budget_reset,
/* .clone = */ common_reasoning_budget_clone,
/* .free = */ common_reasoning_budget_free,
/* .backend_init = */ nullptr,
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
};
static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {
const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx;
return llama_sampler_init(
/* .iface = */ &common_reasoning_budget_i,
/* .ctx = */ new common_reasoning_budget_ctx(*ctx)
);
}
static struct llama_sampler * common_reasoning_budget_init_state(
const struct llama_vocab * vocab,
const std::vector<llama_token> & start_tokens,
const std::vector<llama_token> & end_tokens,
const std::vector<llama_token> & forced_tokens,
int32_t budget,
common_reasoning_budget_state initial_state) {
// promote COUNTING with budget <= 0 to FORCING
if (initial_state == REASONING_BUDGET_COUNTING && budget <= 0) {
initial_state = REASONING_BUDGET_FORCING;
}
return llama_sampler_init(
/* .iface = */ &common_reasoning_budget_i,
/* .ctx = */ new common_reasoning_budget_ctx {
/* .vocab = */ vocab,
/* .start_matcher = */ { start_tokens, 0 },
/* .end_matcher = */ { end_tokens, 0 },
/* .forced_tokens = */ forced_tokens,
/* .budget = */ budget,
/* .remaining = */ budget,
/* .state = */ initial_state,
/* .force_pos = */ 0,
}
);
}
struct llama_sampler * common_reasoning_budget_init(
const struct llama_vocab * vocab,
const std::vector<llama_token> & start_tokens,
const std::vector<llama_token> & end_tokens,
const std::vector<llama_token> & forced_tokens,
int32_t budget,
common_reasoning_budget_state initial_state) {
return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
}
common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl) {
if (!smpl) {
return REASONING_BUDGET_IDLE;
}
return ((const common_reasoning_budget_ctx *)smpl->ctx)->state;
}
bool common_reasoning_budget_force(struct llama_sampler * smpl) {
if (!smpl) {
return false;
}
auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
// only a sampler that is actively counting down the budget may be forced;
// any other state (idle, already forcing/waiting, or done) is left untouched
if (ctx->state != REASONING_BUDGET_COUNTING) {
return false;
}
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
COM_TRC("%s", "forced into forcing state (manual transition)\n");
return true;
}
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