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
| static __global__ void add_id_kernel( | |
| const float * src0, const float * src1, const int32_t * src2, float * dst, | |
| int64_t ne0, int64_t ne1, | |
| size_t nb01, size_t nb02, | |
| size_t nb11, | |
| size_t nb21 | |
| ) { | |
| const int64_t i1 = blockIdx.x; | |
| const int64_t i2 = blockIdx.y; | |
| const int i11 = *(const int32_t *) ((const char *) src2 + i1*sizeof(int32_t) + i2*nb21); | |
| const size_t nb1 = ne0 * sizeof(float); | |
| const size_t nb2 = ne1 * nb1; | |
| float * dst_row = (float *)((char *)dst + i1*nb1 + i2*nb2); | |
| const float * src0_row = (const float *)((const char *)src0 + i1*nb01 + i2*nb02); | |
| const float * src1_row = (const float *)((const char *)src1 + i11*nb11); | |
| for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) { | |
| dst_row[i0] = src0_row[i0] + src1_row[i0]; | |
| } | |
| } | |
| void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { | |
| const ggml_tensor * src0 = dst->src[0]; | |
| const ggml_tensor * src1 = dst->src[1]; | |
| const ggml_tensor * src2 = dst->src[2]; | |
| GGML_TENSOR_TERNARY_OP_LOCALS | |
| GGML_ASSERT(dst->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src1->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src2->type == GGML_TYPE_I32); | |
| GGML_ASSERT(nb00 == sizeof(float)); | |
| GGML_ASSERT(nb10 == sizeof(float)); | |
| GGML_ASSERT(nb20 == sizeof(int32_t)); | |
| const float * src0_d = (const float *)src0->data; | |
| const float * src1_d = (const float *)src1->data; | |
| const int32_t * src2_d = (const int32_t *)src2->data; | |
| float * dst_d = (float *)dst->data; | |
| int threads = std::min((int)ne00, 768); // cols | |
| dim3 blocks(ne01, ne02); // n_experts_used, n_tokens | |
| add_id_kernel<<<blocks, threads, 0, ctx.stream()>>>( | |
| src0_d, src1_d, src2_d, dst_d, | |
| ne0, ne1, | |
| nb01, nb02, | |
| nb11, | |
| nb21 | |
| ); | |
| } | |