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.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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
| 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; | |
| } | |