yahma/alpaca-cleaned
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How to use ar08/tinyllama-nerd-gguf with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ar08/tinyllama-nerd-gguf") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ar08/tinyllama-nerd-gguf", dtype="auto")How to use ar08/tinyllama-nerd-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ar08/tinyllama-nerd-gguf", filename="tinyllama-nerd-gguf-unsloth.Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
How to use ar08/tinyllama-nerd-gguf with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ar08/tinyllama-nerd-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ar08/tinyllama-nerd-gguf:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ar08/tinyllama-nerd-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ar08/tinyllama-nerd-gguf:Q4_K_M
# 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 ar08/tinyllama-nerd-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ar08/tinyllama-nerd-gguf:Q4_K_M
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 ar08/tinyllama-nerd-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ar08/tinyllama-nerd-gguf:Q4_K_M
docker model run hf.co/ar08/tinyllama-nerd-gguf:Q4_K_M
How to use ar08/tinyllama-nerd-gguf with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ar08/tinyllama-nerd-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ar08/tinyllama-nerd-gguf",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ar08/tinyllama-nerd-gguf:Q4_K_M
How to use ar08/tinyllama-nerd-gguf with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ar08/tinyllama-nerd-gguf" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ar08/tinyllama-nerd-gguf",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "ar08/tinyllama-nerd-gguf" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ar08/tinyllama-nerd-gguf",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ar08/tinyllama-nerd-gguf with Ollama:
ollama run hf.co/ar08/tinyllama-nerd-gguf:Q4_K_M
How to use ar08/tinyllama-nerd-gguf with Unsloth Studio:
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 ar08/tinyllama-nerd-gguf to start chatting
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 ar08/tinyllama-nerd-gguf to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ar08/tinyllama-nerd-gguf to start chatting
How to use ar08/tinyllama-nerd-gguf with Docker Model Runner:
docker model run hf.co/ar08/tinyllama-nerd-gguf:Q4_K_M
How to use ar08/tinyllama-nerd-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ar08/tinyllama-nerd-gguf:Q4_K_M
lemonade run user.tinyllama-nerd-gguf-Q4_K_M
lemonade list
To use this model, follow the steps below:
Install the necessary packages:
# Install llama-cpp-python
pip install llama-cpp-python
# Install transformers from source - only needed for versions <= v4.34
pip install git+https://github.com/huggingface/transformers.git
# Install accelerate
pip install accelerate
Instantiate the model:
from llama_cpp import Llama
# Define the model path
my_model_path = "your_downloaded_model_name/path"
CONTEXT_SIZE = 512
# Load the model
model = Llama(model_path=my_model_path, n_ctx=CONTEXT_SIZE)
Generate text from a prompt:
def generate_text_from_prompt(user_prompt, max_tokens=100, temperature=0.3, top_p=0.1, echo=True, stop=["Q", "\n"]):
# Define the parameters
model_output = model(
user_prompt,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
echo=echo,
stop=stop,
)
return model_output["choices"][0]["text"].strip()
if __name__ == "__main__":
my_prompt = "What do you think about the inclusion policies in Tech companies?"
model_response = generate_text_from_prompt(my_prompt)
print(model_response)
4-bit
docker model run hf.co/ar08/tinyllama-nerd-gguf:Q4_K_M