Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

TdAI

File size: 1,017 Bytes

1debbd5

#include "ggml-backend-impl.h"

#if defined(__s390x__)
#include <sys/auxv.h>

// find hwcap bits in asm/elf.h
#ifndef HWCAP_VXRS_EXT2
#define HWCAP_VXRS_EXT2 (1 << 15)
#endif

#ifndef HWCAP_NNPA
#define HWCAP_NNPA (1 << 20)
#endif

struct s390x_features {
    bool has_vxe2 = false;
    bool has_nnpa = false;

    s390x_features() {
        uint32_t hwcap = getauxval(AT_HWCAP);
        // NOTE: use hwcap2 with DFLT for z17 and later
        // uint32_t hwcap2 = getauxval(AT_HWCAP2);

        has_vxe2 = !!(hwcap & HWCAP_VXRS_EXT2);
        has_nnpa = !!(hwcap & HWCAP_NNPA);
    }
};

static int ggml_backend_cpu_s390x_score() {
    int score = 1;
    s390x_features sf;

// IBM z15 / LinuxONE 3
#ifdef GGML_USE_VXE2
    if (!sf.has_vxe2) { return 0; }
    score += 1 << 1;
#endif

// IBM z16 / LinuxONE 4 and z17 / LinuxONE 5
#ifdef GGML_USE_NNPA
    if (!sf.has_nnpa) { return 0; }
    score += 1 << 2;
#endif

    return score;
}

GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_s390x_score)

#endif  // __s390x__