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: 5,657 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 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 | #include "virtgpu-forward-impl.h"
#include "virtgpu-shm.h"
int apir_device_get_count(virtgpu * gpu) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_COUNT);
REMOTE_CALL(gpu, encoder, decoder, ret);
int32_t dev_count = -1;
apir_decode_int32_t(decoder, &dev_count);
remote_call_finish(gpu, encoder, decoder);
return dev_count;
}
char * apir_device_get_name(virtgpu * gpu) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_NAME);
REMOTE_CALL(gpu, encoder, decoder, ret);
const size_t string_size = apir_decode_array_size_unchecked(decoder);
char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
if (!string) {
GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__);
return NULL;
}
apir_decode_char_array(decoder, string, string_size);
remote_call_finish(gpu, encoder, decoder);
return string;
}
char * apir_device_get_description(virtgpu * gpu) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION);
REMOTE_CALL(gpu, encoder, decoder, ret);
const size_t string_size = apir_decode_array_size_unchecked(decoder);
char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
if (!string) {
GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device description buffer\n", __func__);
return NULL;
}
apir_decode_char_array(decoder, string, string_size);
remote_call_finish(gpu, encoder, decoder);
return string;
}
uint32_t apir_device_get_type(virtgpu * gpu) {
static uint32_t dev_type = 255;
if (dev_type != 255) {
return dev_type;
}
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_TYPE);
REMOTE_CALL(gpu, encoder, decoder, ret);
apir_decode_uint32_t(decoder, &dev_type);
remote_call_finish(gpu, encoder, decoder);
return dev_type;
}
void apir_device_get_memory(virtgpu * gpu, size_t * free, size_t * total) {
static size_t dev_free = 0;
static size_t dev_total = 0;
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_MEMORY);
REMOTE_CALL(gpu, encoder, decoder, ret);
apir_decode_size_t(decoder, &dev_free);
apir_decode_size_t(decoder, &dev_total);
*free = dev_free;
*total = dev_total;
remote_call_finish(gpu, encoder, decoder);
return;
}
bool apir_device_supports_op(virtgpu * gpu, const ggml_tensor * op) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP);
apir_encode_ggml_tensor_inline(encoder, op);
REMOTE_CALL(gpu, encoder, decoder, ret);
bool supports_op;
apir_decode_bool_t(decoder, &supports_op);
remote_call_finish(gpu, encoder, decoder);
return supports_op;
}
apir_buffer_type_host_handle_t apir_device_get_buffer_type(virtgpu * gpu) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE);
REMOTE_CALL(gpu, encoder, decoder, ret);
apir_buffer_type_host_handle_t buft_handle;
apir_decode_apir_buffer_type_host_handle_t(decoder, &buft_handle);
remote_call_finish(gpu, encoder, decoder);
return buft_handle;
}
void apir_device_get_props(virtgpu * gpu,
bool * async,
bool * host_buffer,
bool * buffer_from_host_ptr,
bool * events) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_PROPS);
REMOTE_CALL(gpu, encoder, decoder, ret);
apir_decode_bool_t(decoder, async);
apir_decode_bool_t(decoder, host_buffer);
apir_decode_bool_t(decoder, buffer_from_host_ptr);
apir_decode_bool_t(decoder, events);
remote_call_finish(gpu, encoder, decoder);
return;
}
apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, size_t max_tensor_size) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
apir_buffer_context_t buffer_context;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR);
if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) {
GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate %ldb of guest-host shared buffer", __func__, size);
}
apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id);
apir_encode_size_t(encoder, &size);
apir_encode_size_t(encoder, &max_tensor_size);
REMOTE_CALL(gpu, encoder, decoder, ret);
apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle);
buffer_context.buft_host_handle = apir_decode_apir_buffer_type_host_handle(decoder);
remote_call_finish(gpu, encoder, decoder);
return buffer_context;
}
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