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: 697 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 | import sys
from llama_cpp import Llama
if len(sys.argv) < 2:
print("Model path not provided as argument")
print("Eg. Usage: $ python chatapp.py path/to/model.gguf")
sys.exit(1)
llm = Llama(
model_path=sys.argv[1],
n_ctx=512,
n_threads=4,
n_gpu_layers=1,
verbose=False
)
print("Chat with Llama (type 'exit' to quit)\n")
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]: break
prompt = f"### Human: {user_input}\n### Assistant:"
output = llm(
prompt,
max_tokens=100,
stop=["###", "### Human:", "\n###"]
)
response = output["choices"][0]["text"].strip()
print("Bot:", response)
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