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
- 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
- Atomic Chat
File size: 3,187 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 | #include "backend-dispatched.h"
#include "backend-virgl-apir.h"
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include "shared/apir_backend.h"
#include <cstdint>
static uint32_t validate_graph_operation(size_t cgraph_size, uint32_t shmem_res_id, const char * operation) {
if (cgraph_size == 0) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Zero-size computation graph\n", operation);
return 1;
}
// place-holder: validate that the size of shmem_res_id is <= cgraph_size
// need to add another method in the Virgl->APIR callback interface
GGML_UNUSED(shmem_res_id);
return 0; // Valid
}
uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
static bool async_backend_initialized = false;
static bool async_backend;
if (!async_backend_initialized) {
ggml_backend_dev_props props;
dev->iface.get_props(dev, &props);
async_backend = props.caps.async;
async_backend_initialized = true;
}
uint32_t shmem_res_id;
apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id);
const 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__);
apir_decoder_set_fatal(dec);
return 1;
}
size_t cgraph_size;
apir_decode_size_t(dec, &cgraph_size);
if (validate_graph_operation(cgraph_size, shmem_res_id, __func__) != 0) {
apir_decoder_set_fatal(dec);
return 1;
}
apir_decoder secondary_dec = apir_new_decoder((const char *) shmem_data, cgraph_size);
ggml_cgraph * cgraph = apir_decode_ggml_cgraph(&secondary_dec, cgraph_size);
if (!cgraph || apir_decoder_get_fatal(&secondary_dec)) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to deserialize computation graph\n", __func__);
return 1;
}
if (cgraph->n_nodes < 0 || cgraph->n_leafs < 0) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid negative node/leaf count: nodes=%d leafs=%d\n", __func__,
cgraph->n_nodes, cgraph->n_leafs);
return 1;
}
ggml_status status;
#if APIR_BACKEND_CHECK_SUPPORTS_OP == 1
for (int idx = 0; idx < cgraph->n_nodes; idx++) {
ggml_tensor * op = ggml_graph_node(cgraph, idx);
if (dev->iface.supports_op(dev, op)) {
continue;
}
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", __func__, idx,
ggml_op_desc(op));
status = GGML_STATUS_ABORTED;
apir_encode_ggml_status(enc, &status);
return 0;
}
#endif
// Check if backend is properly initialized
if (!bck) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Backend not initialized (bck is null)\n", __func__);
return 1;
}
status = bck->iface.graph_compute(bck, cgraph);
if (async_backend && bck->iface.synchronize) {
bck->iface.synchronize(bck);
}
apir_encode_ggml_status(enc, &status);
return 0;
}
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