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
| static uint32_t validate_buffer_operation(size_t offset, size_t size, const char * operation) { | |
| // Only check for critical integer overflow - no arbitrary size limits | |
| if (offset > SIZE_MAX - size) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Integer overflow in offset+size: %zu + %zu\n", operation, offset, size); | |
| return 1; | |
| } | |
| return 0; // Valid | |
| } | |
| uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| uintptr_t base = (uintptr_t) buffer->iface.get_base(buffer); | |
| apir_encode_uintptr_t(enc, &base); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| ggml_tensor * tensor; | |
| // safe to remove the const qualifier here | |
| tensor = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); | |
| uint32_t shmem_res_id; | |
| apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); | |
| size_t offset; | |
| apir_decode_size_t(dec, &offset); | |
| size_t size; | |
| apir_decode_size_t(dec, &size); | |
| if (validate_buffer_operation(offset, size, __func__) != 0) { | |
| return 1; | |
| } | |
| void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); | |
| if (!shmem_data) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__); | |
| return 1; | |
| } | |
| buffer->iface.set_tensor(buffer, tensor, shmem_data, offset, size); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| const ggml_tensor * tensor; | |
| // safe to remove the const qualifier here | |
| tensor = apir_decode_ggml_tensor(dec); | |
| uint32_t shmem_res_id; | |
| apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); | |
| size_t offset; | |
| apir_decode_size_t(dec, &offset); | |
| size_t size; | |
| apir_decode_size_t(dec, &size); | |
| if (validate_buffer_operation(offset, size, __func__) != 0) { | |
| return 1; | |
| } | |
| void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); | |
| if (!shmem_data) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__); | |
| return 1; | |
| } | |
| buffer->iface.get_tensor(buffer, tensor, shmem_data, offset, size); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| const ggml_tensor * src; | |
| // safe to remove the const qualifier here | |
| src = apir_decode_ggml_tensor(dec); | |
| ggml_tensor * dst = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); | |
| bool ret = buffer->iface.cpy_tensor(buffer, src, (ggml_tensor *) dst); | |
| apir_encode_bool_t(enc, &ret); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| uint8_t value; | |
| apir_decode_uint8_t(dec, &value); | |
| buffer->iface.clear(buffer, value); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| if (!apir_untrack_backend_buffer(buffer)) { | |
| GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: unknown buffer %p\n", __func__, (void *) buffer); | |
| return 1; | |
| } | |
| buffer->iface.free_buffer(buffer); | |
| return 0; | |
| } | |