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: 2,003 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 | #include "virtgpu-forward-impl.h"
static long long current_time_ms() {
timespec ts;
clock_gettime(CLOCK_REALTIME, &ts); // Use CLOCK_MONOTONIC for elapsed time
return (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec;
}
ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) {
apir_encoder * encoder;
apir_decoder * decoder;
ApirForwardReturnCode ret;
REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE);
std::vector<uint8_t> cgraph_data;
size_t cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data);
virtgpu_shmem temp_shmem; // Local storage for large buffers
virtgpu_shmem * shmem = &temp_shmem;
bool using_shared_shmem = false;
if (cgraph_size <= gpu->data_shmem.mmap_size) {
// Lock mutex before using shared data_shmem buffer
if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) {
GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
}
using_shared_shmem = true;
shmem = &gpu->data_shmem;
} else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) {
GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
}
apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id);
apir_encode_size_t(encoder, &cgraph_size);
char * shmem_data = (char *) shmem->mmap_ptr;
apir_encoder secondary_enc = apir_new_encoder(shmem_data, cgraph_size);
apir_encode_cgraph_data(&secondary_enc, cgraph_data);
REMOTE_CALL(gpu, encoder, decoder, ret);
ggml_status status = GGML_STATUS_ABORTED;
apir_decode_ggml_status(decoder, &status);
remote_call_finish(gpu, encoder, decoder);
// Unlock mutex before cleanup
if (using_shared_shmem) {
mtx_unlock(&gpu->data_shmem_mutex);
} else {
virtgpu_shmem_destroy(gpu, shmem);
}
return status;
}
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