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: 4,039 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 | #include "backend-dispatched.h"
#include "backend-virgl-apir.h"
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include <cstdint>
uint32_t backend_device_get_device_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
int32_t dev_count = reg->iface.get_device_count(reg);
apir_encode_int32_t(enc, &dev_count);
return 0;
}
uint32_t backend_device_get_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
int32_t dev_count = reg->iface.get_device_count(reg);
apir_encode_int32_t(enc, &dev_count);
return 0;
}
uint32_t backend_device_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
const char * string = dev->iface.get_name(dev);
const size_t string_size = strlen(string) + 1;
apir_encode_array_size(enc, string_size);
apir_encode_char_array(enc, string, string_size);
return 0;
}
uint32_t backend_device_get_description(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
const char * string = dev->iface.get_description(dev);
const size_t string_size = strlen(string) + 1;
apir_encode_array_size(enc, string_size);
apir_encode_char_array(enc, string, string_size);
return 0;
}
uint32_t backend_device_get_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
uint32_t type = dev->iface.get_type(dev);
apir_encode_uint32_t(enc, &type);
return 0;
}
uint32_t backend_device_get_memory(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
size_t free, total;
dev->iface.get_memory(dev, &free, &total);
apir_encode_size_t(enc, &free);
apir_encode_size_t(enc, &total);
return 0;
}
uint32_t backend_device_supports_op(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec);
bool supports_op = dev->iface.supports_op(dev, op);
apir_encode_bool_t(enc, &supports_op);
return 0;
}
uint32_t backend_device_get_buffer_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
ggml_backend_buffer_type_t bufft = dev->iface.get_buffer_type(dev);
apir_encode_ggml_buffer_type(enc, bufft);
return 0;
}
uint32_t backend_device_get_props(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
ggml_backend_dev_props props;
dev->iface.get_props(dev, &props);
apir_encode_bool_t(enc, &props.caps.async);
apir_encode_bool_t(enc, &props.caps.host_buffer);
apir_encode_bool_t(enc, &props.caps.buffer_from_host_ptr);
apir_encode_bool_t(enc, &props.caps.events);
return 0;
}
uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
uint32_t shmem_res_id;
apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id);
void * shmem_ptr = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
if (!shmem_ptr) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
apir_decoder_set_fatal(dec);
return 1;
}
size_t size;
apir_decode_size_t(dec, &size);
size_t max_tensor_size;
apir_decode_size_t(dec, &max_tensor_size);
ggml_backend_buffer_t buffer;
buffer = dev->iface.buffer_from_host_ptr(dev, shmem_ptr, size, max_tensor_size);
apir_encode_ggml_buffer(enc, buffer);
apir_encode_ggml_buffer_type(enc, buffer->buft);
if (buffer) {
apir_track_backend_buffer(buffer);
}
return 0;
}
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