Instructions to use MebinThattil/tiny-llama-q4_0 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 MebinThattil/tiny-llama-q4_0 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 MebinThattil/tiny-llama-q4_0 # Run inference directly in the terminal: llama cli -hf MebinThattil/tiny-llama-q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MebinThattil/tiny-llama-q4_0 # Run inference directly in the terminal: llama cli -hf MebinThattil/tiny-llama-q4_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf MebinThattil/tiny-llama-q4_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf MebinThattil/tiny-llama-q4_0
Use Docker
docker model run hf.co/MebinThattil/tiny-llama-q4_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use MebinThattil/tiny-llama-q4_0 with Ollama:
ollama run hf.co/MebinThattil/tiny-llama-q4_0
- Unsloth Desktop
- Docker Model Runner
How to use MebinThattil/tiny-llama-q4_0 with Docker Model Runner:
docker model run hf.co/MebinThattil/tiny-llama-q4_0
- Lemonade
How to use MebinThattil/tiny-llama-q4_0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MebinThattil/tiny-llama-q4_0
Run and chat with the model
lemonade run user.tiny-llama-q4_0-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 2,260 Bytes
5d62acd | 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 | import os
import sys
from typing import Any, Dict
# Avoid "LookupError: unknown encoding: ascii" when open() called in a destructor
outnull_file = open(os.devnull, "w")
errnull_file = open(os.devnull, "w")
STDOUT_FILENO = 1
STDERR_FILENO = 2
class suppress_stdout_stderr(object):
# NOTE: these must be "saved" here to avoid exceptions when using
# this context manager inside of a __del__ method
sys = sys
os = os
def __init__(self, disable: bool = True):
self.disable = disable
# Oddly enough this works better than the contextlib version
def __enter__(self):
if self.disable:
return self
self.old_stdout_fileno_undup = STDOUT_FILENO
self.old_stderr_fileno_undup = STDERR_FILENO
self.old_stdout_fileno = self.os.dup(self.old_stdout_fileno_undup)
self.old_stderr_fileno = self.os.dup(self.old_stderr_fileno_undup)
self.old_stdout = self.sys.stdout
self.old_stderr = self.sys.stderr
self.os.dup2(outnull_file.fileno(), self.old_stdout_fileno_undup)
self.os.dup2(errnull_file.fileno(), self.old_stderr_fileno_undup)
self.sys.stdout = outnull_file
self.sys.stderr = errnull_file
return self
def __exit__(self, *_):
if self.disable:
return
# Check if sys.stdout and sys.stderr have fileno method
self.sys.stdout = self.old_stdout
self.sys.stderr = self.old_stderr
self.os.dup2(self.old_stdout_fileno, self.old_stdout_fileno_undup)
self.os.dup2(self.old_stderr_fileno, self.old_stderr_fileno_undup)
self.os.close(self.old_stdout_fileno)
self.os.close(self.old_stderr_fileno)
class MetaSingleton(type):
"""
Metaclass for implementing the Singleton pattern.
"""
_instances: Dict[type, Any] = {}
def __call__(cls, *args: Any, **kwargs: Any) -> Any:
if cls not in cls._instances:
cls._instances[cls] = super(MetaSingleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class Singleton(object, metaclass=MetaSingleton):
"""
Base class for implementing the Singleton pattern.
"""
def __init__(self):
super(Singleton, self).__init__()
|