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
| #pragma once | |
| #include "ggml-common.h" | |
| #include "convert.cuh" | |
| static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) { | |
| if (x <= val[0]) return 0; | |
| if (x >= val[n-1]) return n-1; | |
| int ml = 0, mu = n-1; | |
| while (mu-ml > 1) { | |
| int mav = (ml+mu)/2; | |
| if (x < val[mav]) mu = mav; else ml = mav; | |
| } | |
| return x - val[mu-1] < val[mu] - x ? mu-1 : mu; | |
| } | |
| static __device__ void quantize_f32_q4_0_block(const float * __restrict__ x, block_q4_0 * __restrict__ y) { | |
| float amax = 0.0f; | |
| float vmax = 0.0f; | |
| for (int j = 0; j < QK4_0; ++j) { | |
| const float v = x[j]; | |
| if (amax < fabsf(v)) { | |
| amax = fabsf(v); | |
| vmax = v; | |
| } | |
| } | |
| const float d = vmax / -8; | |
| const float id = d ? 1.0f/d : 0.0f; | |
| y->d = d; | |
| for (int j = 0; j < QK4_0/2; ++j) { | |
| const float x0 = x[0 + j]*id; | |
| const float x1 = x[QK4_0/2 + j]*id; | |
| const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f)); | |
| const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f)); | |
| y->qs[j] = xi0; | |
| y->qs[j] |= xi1 << 4; | |
| } | |
| } | |
| static __device__ void quantize_f32_q4_1_block(const float * __restrict__ x, block_q4_1 * __restrict__ y) { | |
| float vmin = FLT_MAX; | |
| float vmax = -FLT_MAX; | |
| for (int j = 0; j < QK4_1; ++j) { | |
| const float v = x[j]; | |
| if (v < vmin) vmin = v; | |
| if (v > vmax) vmax = v; | |
| } | |
| const float d = (vmax - vmin) / ((1 << 4) - 1); | |
| const float id = d ? 1.0f/d : 0.0f; | |
| y->dm.x = d; | |
| y->dm.y = vmin; | |
| for (int j = 0; j < QK4_1/2; ++j) { | |
| const float x0 = (x[0 + j] - vmin)*id; | |
| const float x1 = (x[QK4_1/2 + j] - vmin)*id; | |
| const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f)); | |
| const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f)); | |
| y->qs[j] = xi0; | |
| y->qs[j] |= xi1 << 4; | |
| } | |
| } | |
| static __device__ void quantize_f32_q5_0_block(const float * __restrict__ x, block_q5_0 * __restrict__ y) { | |
| float amax = 0.0f; | |
| float vmax = 0.0f; | |
| for (int j = 0; j < QK5_0; ++j) { | |
| const float v = x[j]; | |
| if (amax < fabsf(v)) { | |
| amax = fabsf(v); | |
| vmax = v; | |
| } | |
| } | |
| const float d = vmax / -16; | |
| const float id = d ? 1.0f/d : 0.0f; | |
| y->d = d; | |
| uint32_t qh = 0; | |
| for (int j = 0; j < QK5_0/2; ++j) { | |
| const float x0 = x[0 + j]*id; | |
| const float x1 = x[QK5_0/2 + j]*id; | |
| const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f)); | |
| const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f)); | |
| y->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); | |
| qh |= ((xi0 & 0x10u) >> 4) << (j + 0); | |
| qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); | |
| } | |
| memcpy(y->qh, &qh, sizeof(qh)); | |
| } | |
| static __device__ void quantize_f32_q5_1_block(const float * __restrict__ x, block_q5_1 * __restrict__ y) { | |
| float min = x[0]; | |
| float max = x[0]; | |
| for (int j = 1; j < QK5_1; ++j) { | |
| const float v = x[j]; | |
| min = v < min ? v : min; | |
| max = v > max ? v : max; | |
| } | |
| const float d = (max - min) / 31; | |
| const float id = d ? 1.0f/d : 0.0f; | |
| y->dm.x = d; | |
| y->dm.y = min; | |
| uint32_t qh = 0; | |
| for (int j = 0; j < QK5_1/2; ++j) { | |
| const float x0 = (x[0 + j] - min)*id; | |
| const float x1 = (x[QK5_1/2 + j] - min)*id; | |
| const uint8_t xi0 = (uint8_t)(x0 + 0.5f); | |
| const uint8_t xi1 = (uint8_t)(x1 + 0.5f); | |
| y->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); | |
| qh |= ((xi0 & 0x10u) >> 4) << (j + 0); | |
| qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); | |
| } | |
| memcpy(y->qh, &qh, sizeof(qh)); | |
| } | |
| static __device__ void quantize_f32_q8_0_block(const float * __restrict__ x, block_q8_0 * __restrict__ y) { | |
| float amax = 0.0f; // absolute max | |
| for (int j = 0; j < QK8_0; j++) { | |
| const float v = x[j]; | |
| amax = fmaxf(amax, fabsf(v)); | |
| } | |
| const float d = amax / ((1 << 7) - 1); | |
| const float id = d ? 1.0f/d : 0.0f; | |
| y->d = d; | |
| for (int j = 0; j < QK8_0; ++j) { | |
| const float x0 = x[j]*id; | |
| y->qs[j] = roundf(x0); | |
| } | |
| } | |
| static __device__ void quantize_f32_iq4_nl_block(const float * __restrict__ x, block_iq4_nl * __restrict__ y) { | |
| float amax = 0.0f; | |
| float vmax = 0.0f; | |
| for (int j = 0; j < QK4_NL; ++j) { | |
| const float v = x[j]; | |
| if (amax < fabsf(v)) { | |
| amax = fabsf(v); | |
| vmax = v; | |
| } | |
| } | |
| float d = vmax / kvalues_iq4nl[0]; | |
| const float id = d ? 1.0f/d : 0.0f; | |
| float sumqx = 0, sumq2 = 0; | |
| for (int j = 0; j < QK4_NL/2; ++j) { | |
| const float x0 = x[0 + j]*id; | |
| const float x1 = x[QK4_NL/2 + j]*id; | |
| const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0); | |
| const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1); | |
| y->qs[j] = xi0 | (xi1 << 4); | |
| const float v0 = kvalues_iq4nl[xi0]; | |
| const float v1 = kvalues_iq4nl[xi1]; | |
| const float w0 = x[0 + j]*x[0 + j]; | |
| const float w1 = x[QK4_NL/2 + j]*x[QK4_NL/2 + j]; | |
| sumqx += w0*v0*x[j] + w1*v1*x[QK4_NL/2 + j]; | |
| sumq2 += w0*v0*v0 + w1*v1*v1; | |
| } | |
| y->d = sumq2 > 0 ? sumqx/sumq2 : d; | |
| } | |
| // Wrapper functions for cpy.cu compatibility | |
| static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { | |
| quantize_f32_q4_0_block((const float *)cxi, (block_q4_0 *)cdsti); | |
| } | |
| static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { | |
| quantize_f32_q4_1_block((const float *)cxi, (block_q4_1 *)cdsti); | |
| } | |
| static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) { | |
| quantize_f32_q5_0_block((const float *)cxi, (block_q5_0 *)cdsti); | |
| } | |
| static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) { | |
| quantize_f32_q5_1_block((const float *)cxi, (block_q5_1 *)cdsti); | |
| } | |
| static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { | |
| quantize_f32_q8_0_block((const float *)cxi, (block_q8_0 *)cdsti); | |
| } | |
| static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) { | |
| quantize_f32_iq4_nl_block((const float *)cxi, (block_iq4_nl *)cdsti); | |
| } | |
| template<typename src_t, typename dst_t> | |
| static __device__ void cpy_1_scalar(const char * cxi, char * cdsti) { | |
| *(dst_t *) cdsti = ggml_cuda_cast<dst_t>(*(const src_t *) cxi); | |
| } | |