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 __global__ void acc_f32(const float * x, const float * y, float * dst, const int64_t ne, | |
| const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, | |
| const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) { | |
| const int64_t i = blockDim.x * blockIdx.x + threadIdx.x; | |
| if (i >= ne) { | |
| return; | |
| } | |
| int64_t src1_idx = i - offset; | |
| int64_t tmp = src1_idx; | |
| const int64_t i13 = tmp / s13; | |
| tmp -= i13 * s13; | |
| const int64_t i12 = tmp / s12; | |
| tmp -= i12 * s12; | |
| const int64_t i11 = tmp / s11; | |
| tmp -= i11 * s11; | |
| const int64_t i10 = tmp; | |
| float val = x[i]; | |
| if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) { | |
| val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10]; | |
| } | |
| dst[i] = val; | |
| } | |
| static void acc_f32_cuda(const float * x, const float * y, float * dst, const int64_t n_elements, | |
| const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, | |
| const int64_t s1, const int64_t s2, const int64_t s3, const int64_t offset, cudaStream_t stream) { | |
| const int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE; | |
| acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset); | |
| } | |
| void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { | |
| const ggml_tensor * src0 = dst->src[0]; | |
| const ggml_tensor * src1 = dst->src[1]; | |
| const float * src0_d = (const float *) src0->data; | |
| const float * src1_d = (const float *) src1->data; | |
| float * dst_d = (float *) dst->data; | |
| cudaStream_t stream = ctx.stream(); | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| GGML_ASSERT(src1->type == GGML_TYPE_F32); | |
| GGML_ASSERT( dst->type == GGML_TYPE_F32); | |
| GGML_ASSERT(ggml_is_contiguous(src1)); | |
| GGML_ASSERT(dst->nb[0] == ggml_element_size(dst)); | |
| GGML_ASSERT(ggml_is_contiguously_allocated(dst)); | |
| const int64_t s1 = dst->op_params[0] / sizeof(float); | |
| const int64_t s2 = dst->op_params[1] / sizeof(float); | |
| const int64_t s3 = dst->op_params[2] / sizeof(float); | |
| const int64_t offset = dst->op_params[3] / sizeof(float); | |
| acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], s1, s2, s3, offset, stream); | |
| } | |