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: 1,309 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 | import sys
import ctypes
import logging
import llama_cpp
# enum ggml_log_level {
# GGML_LOG_LEVEL_NONE = 0,
# GGML_LOG_LEVEL_INFO = 1,
# GGML_LOG_LEVEL_WARN = 2,
# GGML_LOG_LEVEL_ERROR = 3,
# GGML_LOG_LEVEL_DEBUG = 4,
# GGML_LOG_LEVEL_CONT = 5, // continue previous log
# };
GGML_LOG_LEVEL_TO_LOGGING_LEVEL = {
0: logging.CRITICAL,
1: logging.INFO,
2: logging.WARNING,
3: logging.ERROR,
4: logging.DEBUG,
5: logging.DEBUG,
}
logger = logging.getLogger("llama-cpp-python")
_last_log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[0]
# typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
@llama_cpp.llama_log_callback
def llama_log_callback(
level: int,
text: bytes,
user_data: ctypes.c_void_p,
):
# TODO: Correctly implement continue previous log
global _last_log_level
log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level
if logger.level <= GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level]:
print(text.decode("utf-8"), end="", flush=True, file=sys.stderr)
_last_log_level = log_level
llama_cpp.llama_log_set(llama_log_callback, ctypes.c_void_p(0))
def set_verbose(verbose: bool):
logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
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