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
| #include "common.cuh" | |
| static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q1_0 * x = (const block_q1_0 *) vx; | |
| const float d = x[ib].d; | |
| const int bit_index_0 = iqs; | |
| const int bit_index_1 = iqs + 1; | |
| const int byte_index_0 = bit_index_0 / 8; | |
| const int bit_offset_0 = bit_index_0 % 8; | |
| const int byte_index_1 = bit_index_1 / 8; | |
| const int bit_offset_1 = bit_index_1 % 8; | |
| // Extract bits: 1 = +d, 0 = -d (branchless) | |
| const int bit_0 = (x[ib].qs[byte_index_0] >> bit_offset_0) & 1; | |
| const int bit_1 = (x[ib].qs[byte_index_1] >> bit_offset_1) & 1; | |
| v.x = (2*bit_0 - 1) * d; | |
| v.y = (2*bit_1 - 1) * d; | |
| } | |
| static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q4_0 * x = (const block_q4_0 *) vx; | |
| const float d = x[ib].d; | |
| const int vui = x[ib].qs[iqs]; | |
| v.x = vui & 0xF; | |
| v.y = vui >> 4; | |
| v.x = (v.x - 8.0f) * d; | |
| v.y = (v.y - 8.0f) * d; | |
| } | |
| static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q4_1 * x = (const block_q4_1 *) vx; | |
| const float2 dm = __half22float2(x[ib].dm); | |
| const int vui = x[ib].qs[iqs]; | |
| v.x = vui & 0xF; | |
| v.y = vui >> 4; | |
| v.x = (v.x * dm.x) + dm.y; | |
| v.y = (v.y * dm.x) + dm.y; | |
| } | |
| static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q5_0 * x = (const block_q5_0 *) vx; | |
| const float d = x[ib].d; | |
| uint32_t qh; | |
| memcpy(&qh, x[ib].qh, sizeof(qh)); | |
| const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; | |
| const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; | |
| v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); | |
| v.y = ((x[ib].qs[iqs] >> 4) | xh_1); | |
| v.x = (v.x - 16.0f) * d; | |
| v.y = (v.y - 16.0f) * d; | |
| } | |
| static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q5_1 * x = (const block_q5_1 *) vx; | |
| const float2 dm = __half22float2(x[ib].dm); | |
| uint32_t qh; | |
| memcpy(&qh, x[ib].qh, sizeof(qh)); | |
| const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; | |
| const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; | |
| v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); | |
| v.y = ((x[ib].qs[iqs] >> 4) | xh_1); | |
| v.x = (v.x * dm.x) + dm.y; | |
| v.y = (v.y * dm.x) + dm.y; | |
| } | |
| static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ | |
| const block_q8_0 * x = (const block_q8_0 *) vx; | |
| const float d = x[ib].d; | |
| v.x = x[ib].qs[iqs + 0]; | |
| v.y = x[ib].qs[iqs + 1]; | |
| v.x *= d; | |
| v.y *= d; | |
| } | |