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: 5,435 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 | #include "ggml-remoting.h"
#define BUFFER_TO_GPU(name) ((ggml_backend_remoting_buffer_context *) (name)->context)->gpu
static void * ggml_backend_remoting_buffer_get_base(ggml_backend_buffer_t buffer) {
ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) buffer->context;
if (context->base) {
return context->base;
}
context->base = apir_buffer_get_base(BUFFER_TO_GPU(buffer), BUFFER_TO_APIR_CONTEXT(buffer));
return context->base;
}
static void ggml_backend_remoting_buffer_set_tensor(ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
if (context->is_from_ptr) {
memcpy((char *) tensor->data + offset, data, size);
} else {
apir_buffer_set_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size);
}
return;
}
static void ggml_backend_remoting_buffer_get_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
if (context->is_from_ptr) {
memcpy(data, (const char *) tensor->data + offset, size);
} else {
apir_buffer_get_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size);
}
}
static void ggml_backend_remoting_buffer_set_tensor_from_ptr(ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
UNUSED(buffer);
memcpy((char *) tensor->data + offset, data, size);
return;
}
static void ggml_backend_remoting_buffer_get_tensor_from_ptr(ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
UNUSED(buffer);
memcpy(data, (const char *) tensor->data + offset, size);
}
static bool ggml_backend_remoting_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor * src,
ggml_tensor * dst) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
bool ret = apir_buffer_cpy_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), src, dst);
return ret;
}
static void ggml_backend_remoting_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
apir_buffer_clear(gpu, BUFFER_TO_APIR_CONTEXT(buffer), value);
return;
}
static void ggml_backend_remoting_buffer_free_buffer(ggml_backend_buffer_t buffer) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
apir_buffer_free_buffer(gpu, BUFFER_TO_APIR_CONTEXT(buffer));
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
free(context);
buffer->context = NULL;
}
const ggml_backend_buffer_i ggml_backend_remoting_buffer_interface = {
/* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer,
/* .get_base = */ ggml_backend_remoting_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor,
/* .set_tensor_2d = */ NULL,
/* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor,
/* .clear = */ ggml_backend_remoting_buffer_clear,
/* .reset = */ NULL,
};
const ggml_backend_buffer_i ggml_backend_remoting_buffer_from_ptr_interface = {
/* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer,
/* .get_base = */ ggml_backend_remoting_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor_from_ptr,
/* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor_from_ptr,
/* .set_tensor_2d = */ NULL,
/* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor,
/* .clear = */ ggml_backend_remoting_buffer_clear,
/* .reset = */ NULL,
};
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