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
- 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: 5,381 Bytes
0862f9d | 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 | #include <cstdio>
#include <type_traits>
#include <vector>
#include <random>
#include <chrono>
#include <cstdlib>
#include <cmath>
#include <cassert>
#include <cstring>
#include <array>
#include <type_traits>
#include <ggml.h>
#include <ggml-cpu.h>
constexpr int kVecSize = 1 << 16;
// Copy-pasted from ggml.c
#define QK4_0 32
typedef struct {
float d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
float d; // delta
float m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
// Copy-pasted from ggml.c
#define QK8_0 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == 2*sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(QK4_1 == QK8_0, "QK4_1 and QK8_0 must be the same");
static_assert(QK4_0 == QK8_0, "QK4_0 and QK8_0 must be the same");
template <typename T>
static void fillQ4blocks(std::vector<T>& blocks, std::mt19937& rndm) {
for (auto& b : blocks) {
b.d = 1;
for (int i=0; i<QK4_1/2; ++i) {
uint8_t v1 = rndm() >> 28;
uint8_t v2 = rndm() >> 28;
b.qs[i] = v1 | (v2 << 4);
}
}
}
static void fillQ80blocks(std::vector<block_q8_0>& blocks, std::mt19937& rndm) {
for (auto& b : blocks) {
b.d = 1;
int sum = 0;
for (int i=0; i<QK8_0; ++i) {
b.qs[i] = (rndm() >> 24) - 128;
sum += b.qs[i];
}
b.s = b.d * sum;
}
}
static float simpleDot(const block_q4_0& x, const block_q8_0& y) {
int s1 = 0; //, s2 = 0;
for (int i=0; i<QK4_1/2; i+=2) {
int v1 = x.qs[i+0] & 0xf;
int v2 = x.qs[i+0] >> 4;
int v3 = x.qs[i+1] & 0xf;
int v4 = x.qs[i+1] >> 4;
int j = 2*i;
s1 += v1*y.qs[j] + v2*y.qs[j+1] + v3*y.qs[j+2] + v4*y.qs[j+3];
//s2 += y.qs[j] + y.qs[j+1] + y.qs[j+2] + y.qs[j+3];
}
return y.d * x.d * s1 - 8 * x.d * y.s;
//return y.d * x.d * (s1 - 8 * s2);
}
static float simpleDot(const block_q4_1& x, const block_q8_0& y) {
int s1 = 0; //, s2 = 0;
for (int i=0; i<QK4_1/2; i+=2) {
int v1 = x.qs[i+0] & 0xf;
int v2 = x.qs[i+0] >> 4;
int v3 = x.qs[i+1] & 0xf;
int v4 = x.qs[i+1] >> 4;
int j = 2*i;
s1 += v1*y.qs[j] + v2*y.qs[j+1] + v3*y.qs[j+2] + v4*y.qs[j+3];
//s2 += y.qs[j] + y.qs[j+1] + y.qs[j+2] + y.qs[j+3];
}
return y.d * x.d * s1 + y.s * x.m;
//return y.d * (x.d * s1 + x.m * s2);
}
struct Stat {
double sum = 0, sumt = 0, sumt2 = 0, maxt = 0;
int nloop = 0;
void addResult(double s, double t) {
sum += s;
sumt += t; sumt2 += t*t; maxt = std::max(maxt, t);
++nloop;
}
void reportResult(const char* title) const {
if (nloop < 1) {
printf("%s(%s): no result\n",__func__,title);
return;
}
printf("============ %s\n",title);
printf("<dot> = %g\n",sum/nloop);
auto t = sumt/nloop, dt = sumt2/nloop - t*t;
if (dt > 0) dt = sqrt(dt);
printf("<time> = %g +/- %g us. Max. time = %g us.\n",t,dt,maxt);
}
};
int main(int argc, char** argv) {
int nloop = argc > 1 ? atoi(argv[1]) : 10;
int type = argc > 2 ? atoi(argv[2]) : 1;
std::mt19937 rndm(1234);
std::vector<block_q4_1> x41;
std::vector<block_q4_0> x40;
std::vector<block_q8_0> y(kVecSize);
if (type == 0) x40.resize(kVecSize);
else {
x41.resize(kVecSize);
for (auto& b : x41) b.m = 1;
}
auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
const auto * funcs = ggml_get_type_traits_cpu(ggml_type);
Stat simple, ggml;
for (int iloop=0; iloop<nloop; ++iloop) {
if (type == 0) fillQ4blocks(x40, rndm);
else fillQ4blocks(x41, rndm);
fillQ80blocks(y, rndm);
auto t1 = std::chrono::high_resolution_clock::now();
double s = 0;
if (type == 0) for (int i=0; i<kVecSize; ++i) s += simpleDot(x40[i], y[i]);
else for (int i=0; i<kVecSize; ++i) s += simpleDot(x41[i], y[i]);
auto t2 = std::chrono::high_resolution_clock::now();
auto t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
if (iloop > 3) simple.addResult(s, t);
t1 = std::chrono::high_resolution_clock::now();
float fs;
if (type == 0) funcs->vec_dot(kVecSize * QK4_1, &fs, 0, x40.data(), 0, y.data(), 0, 1);
else funcs->vec_dot(kVecSize * QK4_1, &fs, 0, x41.data(), 0, y.data(), 0, 1);
t2 = std::chrono::high_resolution_clock::now();
t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
if (iloop > 3) ggml.addResult(fs, t);
}
// Report the time (and the average of the dot products so the compiler does not come up with the idea
// of optimizing away the function calls after figuring that the result is not used).
simple.reportResult("Simple");
ggml.reportResult("ggml");
return 0;
}
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