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.
- 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: 4,792 Bytes
0862f9d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | """Shared helpers for QDC on-device test runners."""
from __future__ import annotations
import logging
import os
import subprocess
import tempfile
from appium.options.common import AppiumOptions
log = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# On-device paths
# ---------------------------------------------------------------------------
BUNDLE_PATH = "/data/local/tmp/llama.cpp"
BIN_PATH = f"{BUNDLE_PATH}/bin"
LIB_PATH = f"{BUNDLE_PATH}/lib"
QDC_LOGS_PATH = "/data/local/tmp/QDC_logs"
SCRIPTS_DIR = "/qdc/appium"
MODEL_NAME = "model.gguf"
MODEL_DEVICE_PATH = "/data/local/tmp/gguf/model.gguf"
PROMPT_DIR = "/data/local/tmp/scorecard_prompts"
# ---------------------------------------------------------------------------
# Appium session options
# ---------------------------------------------------------------------------
options = AppiumOptions()
options.set_capability("automationName", "UiAutomator2")
options.set_capability("platformName", "Android")
options.set_capability("deviceName", os.getenv("ANDROID_DEVICE_VERSION"))
# ---------------------------------------------------------------------------
# Shell / process helpers
# ---------------------------------------------------------------------------
def write_qdc_log(filename: str, content: str) -> None:
"""Write content as a log file for QDC log collection."""
subprocess.run(
["adb", "shell", f"mkdir -p {QDC_LOGS_PATH}"],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
with tempfile.NamedTemporaryFile(mode="w", suffix=".log", delete=False) as f:
f.write(content)
tmp_path = f.name
try:
subprocess.run(
["adb", "push", tmp_path, f"{QDC_LOGS_PATH}/{filename}"],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
finally:
os.unlink(tmp_path)
def ensure_bundle(check_binary: str | None = None) -> None:
"""Ensure the llama_cpp_bundle is available on the target device."""
push_bundle_if_needed(check_binary or f"{BIN_PATH}/llama-cli")
# ---------------------------------------------------------------------------
# Android / Linux host helpers
# ---------------------------------------------------------------------------
def run_adb_command(cmd: str, *, check: bool = True) -> subprocess.CompletedProcess:
"""Run a command on-device via ``adb shell`` with exit-code sentinel."""
raw = subprocess.run(
["adb", "shell", f"{cmd}; echo __RC__:$?"],
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
stdout = raw.stdout
returncode = raw.returncode
if stdout:
lines = stdout.rstrip("\n").split("\n")
if lines and lines[-1].startswith("__RC__:"):
try:
returncode = int(lines[-1][7:])
stdout = "\n".join(lines[:-1]) + "\n"
except ValueError:
pass
log.info(stdout)
result = subprocess.CompletedProcess(raw.args, returncode, stdout=stdout)
if check:
assert returncode == 0, f"Command failed (exit {returncode})"
return result
def run_script(
script: str,
extra_env: dict[str, str] | None = None,
extra_args: list[str] | None = None,
) -> subprocess.CompletedProcess:
"""Run an upstream shell script from /qdc/appium/ on the QDC runner host."""
env = os.environ.copy()
env["GGML_HEXAGON_EXPERIMENTAL"] = "1"
if extra_env:
env.update(extra_env)
cmd = [f"{SCRIPTS_DIR}/{script}"] + (extra_args or [])
result = subprocess.run(
cmd, env=env,
text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
)
log.info(result.stdout)
return result
def adb_shell(cmd: str) -> None:
"""Run a command via adb shell (fire-and-forget, no error check)."""
subprocess.run(
["adb", "shell", "sh", "-c", cmd],
capture_output=True, encoding="utf-8", errors="replace", check=False,
)
def push_bundle_if_needed(check_binary: str) -> None:
"""Push llama_cpp_bundle to the device if check_binary is not already present."""
result = subprocess.run(
["adb", "shell", f"ls {check_binary}"],
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
if result.returncode != 0:
subprocess.run(
["adb", "push", "/qdc/appium/llama_cpp_bundle/", BUNDLE_PATH],
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
subprocess.run(
["adb", "shell", f"find {BUNDLE_PATH}/bin -type f -exec chmod 755 {{}} +"],
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
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