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: 2,601 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 | #include "../../include/ggml-virtgpu.h"
#include "ggml-remoting.h"
static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) {
UNUSED(backend);
return "API Remoting backend";
}
static void ggml_backend_remoting_free(ggml_backend_t backend) {
delete backend;
}
static ggml_status ggml_backend_remoting_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
virtgpu * gpu = DEV_TO_GPU(backend->device);
return apir_backend_graph_compute(gpu, cgraph);
}
static void ggml_backend_remoting_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
virtgpu * gpu = DEV_TO_GPU(backend->device);
#if true
UNUSED(gpu);
UNUSED(cgraph);
#else
// not working yet
apir_backend_graph_optimize(gpu, cgraph);
#endif
}
static ggml_backend_i ggml_backend_remoting_interface = {
/* .get_name = */ ggml_backend_remoting_get_name,
/* .free = */ ggml_backend_remoting_free,
/* .set_tensor_async = */ NULL, // ggml_backend_remoting_set_tensor_async,
/* .get_tensor_async = */ NULL, // ggml_backend_remoting_get_tensor_async,
/* .set_tensor_2d_async = */ NULL,
/* .get_tensor_2d_async = */ NULL,
/* .cpy_tensor_async = */ NULL, // ggml_backend_remoting_cpy_tensor_async,
/* .synchronize = */ NULL, // ggml_backend_remoting_synchronize,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_remoting_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .graph_optimize = */ ggml_backend_remoting_graph_optimize,
};
static ggml_guid_t ggml_backend_remoting_guid() {
static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x14, 0x03, 0x86, 0x02,
0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
return &guid;
}
ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params) {
UNUSED(params);
ggml_backend_remoting_device_context * ctx = (ggml_backend_remoting_device_context *) dev->context;
ggml_backend_t remoting_backend = new ggml_backend{
/* .guid = */ ggml_backend_remoting_guid(),
/* .interface = */ ggml_backend_remoting_interface,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_virtgpu_reg(), ctx->device),
/* .context = */ ctx,
};
return remoting_backend;
}
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