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: 8,285 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | #include "llama-kv-cache-dsa.h"
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-model.h"
#include <algorithm>
#include <cassert>
//
// llama_kv_cache_dsa
//
llama_kv_cache_dsa::llama_kv_cache_dsa(
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_reuse_cb & reuse) :
hparams_lid(model.hparams), n_stream(unified ? 1 : n_seq_max) {
LLAMA_LOG_INFO("%s: creating main KV cache, size = %u cells\n", __func__, kv_size);
kv_mla = std::make_unique<llama_kv_cache>(
model, model.hparams, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter, reuse, nullptr);
// we use llama_kv_cache for caching indexer keys
// by hand-tweaking some hparams we fool it to create
// indexer key cache tensors with correct dimensions
// https://github.com/ggml-org/llama.cpp/pull/21149#discussion_r3015940823
// DSA lightning indexer uses MQA with single key head
std::fill(hparams_lid.n_head_kv_arr.begin(), hparams_lid.n_head_kv_arr.end(), 1);
hparams_lid.n_embd_head_k_full = model.hparams.indexer_head_size;
hparams_lid.rope_type = LLAMA_ROPE_TYPE_NEOX;
LLAMA_LOG_INFO("%s: creating indexer KV cache, size = %u cells\n", __func__, kv_size);
kv_lid = std::make_unique<llama_kv_cache>(
model, hparams_lid, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter, reuse, nullptr);
}
void llama_kv_cache_dsa::clear(bool data) {
kv_mla->clear(data);
kv_lid->clear(data);
}
bool llama_kv_cache_dsa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
bool res = true;
res = res & kv_mla->seq_rm(seq_id, p0, p1);
res = res & kv_lid->seq_rm(seq_id, p0, p1);
return res;
}
void llama_kv_cache_dsa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
kv_mla->seq_cp(seq_id_src, seq_id_dst, p0, p1);
kv_lid->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_cache_dsa::seq_keep(llama_seq_id seq_id) {
kv_mla->seq_keep(seq_id);
kv_lid->seq_keep(seq_id);
}
void llama_kv_cache_dsa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
kv_mla->seq_add(seq_id, p0, p1, shift);
kv_lid->seq_add(seq_id, p0, p1, shift);
}
void llama_kv_cache_dsa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
kv_mla->seq_div(seq_id, p0, p1, d);
kv_lid->seq_div(seq_id, p0, p1, d);
}
llama_pos llama_kv_cache_dsa::seq_pos_min(llama_seq_id seq_id) const {
return kv_mla->seq_pos_min(seq_id);
}
llama_pos llama_kv_cache_dsa::seq_pos_max(llama_seq_id seq_id) const {
return kv_mla->seq_pos_max(seq_id);
}
std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsa::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> mb = kv_mla->memory_breakdown();
for (const auto & buft_size : kv_lid->memory_breakdown()) {
mb[buft_size.first] += buft_size.second;
}
return mb;
}
llama_memory_context_ptr llama_kv_cache_dsa::init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
GGML_UNUSED(embd_all);
do {
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
if (balloc.get_n_used() < balloc.get_n_tokens()) {
// failed to find a suitable split
break;
}
auto sinfos_mla = kv_mla->prepare(ubatches);
if (sinfos_mla.empty()) {
break;
}
auto sinfos_lid = kv_lid->prepare(ubatches);
if (sinfos_lid.empty()) {
break;
}
assert(sinfos_mla.size() == sinfos_lid.size());
return std::make_unique<llama_kv_cache_dsa_context>(
this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_dsa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_context_ptr llama_kv_cache_dsa::init_full() {
return std::make_unique<llama_kv_cache_dsa_context>(this);
}
llama_memory_context_ptr llama_kv_cache_dsa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_dsa_context>(this, lctx, optimize);
}
bool llama_kv_cache_dsa::get_can_shift() const {
return kv_mla->get_can_shift() &&
kv_lid->get_can_shift() &&
kv_mla->get_size() == kv_lid->get_size();
}
void llama_kv_cache_dsa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
kv_mla->state_write(io, seq_id, flags);
kv_lid->state_write(io, seq_id, flags);
}
void llama_kv_cache_dsa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
kv_mla->state_read(io, seq_id, flags);
kv_lid->state_read(io, seq_id, flags);
}
llama_kv_cache * llama_kv_cache_dsa::get_mla() const {
return kv_mla.get();
}
llama_kv_cache * llama_kv_cache_dsa::get_lid() const {
return kv_lid.get();
}
//
// llama_kv_cache_dsa_context
//
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(llama_memory_status status) : status(status) {}
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv) :
ctx_mla(kv->get_mla()->init_full()),
ctx_lid(kv->get_lid()->init_full()),
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
}
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv,
llama_context * lctx,
bool optimize) :
ctx_mla(kv->get_mla()->init_update(lctx, optimize)),
ctx_lid(kv->get_lid()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
}
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
llama_kv_cache_dsa * kv,
slot_info_vec_t sinfos_mla,
slot_info_vec_t sinfos_lid,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
ctx_mla(new llama_kv_cache_context(kv->get_mla(), std::move(sinfos_mla), this->ubatches)),
ctx_lid(new llama_kv_cache_context(kv->get_lid(), std::move(sinfos_lid), this->ubatches)),
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
}
llama_kv_cache_dsa_context:: ~llama_kv_cache_dsa_context() = default;
bool llama_kv_cache_dsa_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
ctx_mla->next();
ctx_lid->next();
if (++i_next >= ubatches.size()) {
return false;
}
return true;
}
bool llama_kv_cache_dsa_context::apply() {
assert(!llama_memory_status_is_fail(status));
bool res = true;
res = res & ctx_mla->apply();
res = res & ctx_lid->apply();
return res;
}
llama_memory_status llama_kv_cache_dsa_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_dsa_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_context * llama_kv_cache_dsa_context::get_mla() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_context *>(ctx_mla.get());
}
const llama_kv_cache_context * llama_kv_cache_dsa_context::get_lid() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_context *>(ctx_lid.get());
}
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