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: 2,186 Bytes
c9c3262 | 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 | #pragma once
#include <atomic>
#include <cassert>
#include <cerrno>
#include <cstdarg>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <ctime>
#define unlikely(x) __builtin_expect(!!(x), 0)
#define likely(x) __builtin_expect(!!(x), 1)
#ifndef UNUSED
# define UNUSED(x) (void) (x)
#endif
/** Checks is a value is a power of two. Does not handle zero. */
#define IS_POT(v) (((v) & ((v) - 1)) == 0)
/** Checks is a value is a power of two. Zero handled. */
#define IS_POT_NONZERO(v) ((v) != 0 && IS_POT(v))
/** Align a value to a power of two */
#define ALIGN_POT(x, pot_align) (((x) + (pot_align) - 1) & ~((pot_align) - 1))
#define p_atomic_read(_v) __atomic_load_n((_v), __ATOMIC_ACQUIRE)
static inline bool util_is_power_of_two_nonzero64(uint64_t v) {
return IS_POT_NONZERO(v);
}
static inline uint64_t align64(uint64_t value, uint64_t alignment) {
assert(util_is_power_of_two_nonzero64(alignment));
return ALIGN_POT(value, alignment);
}
struct list_head {
list_head * prev;
list_head * next;
};
struct util_sparse_array {
size_t elem_size;
unsigned node_size_log2;
uintptr_t root;
};
void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx);
void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size);
inline void os_time_sleep(int64_t usecs) {
timespec time;
time.tv_sec = usecs / 1000000;
time.tv_nsec = (usecs % 1000000) * 1000;
while (clock_nanosleep(CLOCK_MONOTONIC, 0, &time, &time) == EINTR)
;
}
struct timer_data {
long long start;
long long total;
long long count;
};
static inline void start_timer(timer_data * timer) {
timespec ts;
clock_gettime(CLOCK_MONOTONIC, &ts);
timer->start = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec;
}
// returns the duration in ns
static inline long long stop_timer(timer_data * timer) {
timespec ts;
clock_gettime(CLOCK_MONOTONIC, &ts);
long long timer_end = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec;
long long duration = (timer_end - timer->start);
timer->total += duration;
timer->count += 1;
return duration;
}
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