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
File size: 2,387 Bytes
1debbd5 | 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 | #include "ggml-backend-impl.h"
#if defined(__aarch64__)
#if defined(__linux__)
#include <sys/auxv.h>
#elif defined(__APPLE__)
#include <sys/sysctl.h>
#endif
#if !defined(HWCAP2_SVE2)
#define HWCAP2_SVE2 (1 << 1)
#endif
#if !defined(HWCAP2_I8MM)
#define HWCAP2_I8MM (1 << 13)
#endif
#if !defined(HWCAP2_SME)
#define HWCAP2_SME (1 << 23)
#endif
struct aarch64_features {
// has_neon not needed, aarch64 has NEON guaranteed
bool has_dotprod = false;
bool has_fp16_va = false;
bool has_sve = false;
bool has_sve2 = false;
bool has_i8mm = false;
bool has_sme = false;
aarch64_features() {
#if defined(__linux__)
uint32_t hwcap = getauxval(AT_HWCAP);
uint32_t hwcap2 = getauxval(AT_HWCAP2);
has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
has_fp16_va = !!(hwcap & HWCAP_FPHP);
has_sve = !!(hwcap & HWCAP_SVE);
has_sve2 = !!(hwcap2 & HWCAP2_SVE2);
has_i8mm = !!(hwcap2 & HWCAP2_I8MM);
has_sme = !!(hwcap2 & HWCAP2_SME);
#elif defined(__APPLE__)
int oldp = 0;
size_t size = sizeof(oldp);
if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) == 0) {
has_dotprod = static_cast<bool>(oldp);
}
if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) == 0) {
has_i8mm = static_cast<bool>(oldp);
}
if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) == 0) {
has_sme = static_cast<bool>(oldp);
}
// Apple apparently does not implement SVE yet
#endif
}
};
static int ggml_backend_cpu_aarch64_score() {
int score = 1;
aarch64_features af;
#ifdef GGML_USE_DOTPROD
if (!af.has_dotprod) { return 0; }
score += 1<<1;
#endif
#ifdef GGML_USE_FP16_VECTOR_ARITHMETIC
if (!af.has_fp16_va) { return 0; }
score += 1<<2;
#endif
#ifdef GGML_USE_SVE
if (!af.has_sve) { return 0; }
score += 1<<3;
#endif
#ifdef GGML_USE_MATMUL_INT8
if (!af.has_i8mm) { return 0; }
score += 1<<4;
#endif
#ifdef GGML_USE_SVE2
if (!af.has_sve2) { return 0; }
score += 1<<5;
#endif
#ifdef GGML_USE_SME
if (!af.has_sme) { return 0; }
score += 1<<6;
#endif
return score;
}
GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_aarch64_score)
# endif // defined(__aarch64__)
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