This repository provides a set of ROS 2 packages to integrate llama.cpp into ROS 2. Using the llama_ros packages, you can easily incorporate the powerful optimization capabilities of llama.cpp into your ROS 2 projects by running GGUF-based LLMs and VLMs.
- chatbot_ros → This chatbot, integrated into ROS 2, uses whisper_ros, to listen to people speech; and llama_ros, to generate responses. The chatbot is controlled by a state machine created with YASMIN.
- explainable_ros → A ROS 2 tool to explain the behavior of a robot. Using the integration of LangChain, logs are stored in a vector database. Then, RAG is applied to retrieve relevant logs for user questions answered with llama_ros.
To run llama_ros with CUDA, first, you must install the CUDA Toolkit.
$ cd ~/ros2_ws/src
$ git clone https://github.com/mgonzs13/llama_ros.git
$ pip3 install -r llama_ros/requirements.txt
$ cd ~/ros2_ws
$ rosdep install --from-paths src --ignore-src -r -y
$ colcon build --cmake-args -DGGML_CUDA=ON # add this for CUDA
Commands are included in llama_ros to speed up the test of GGUF-based LLMs within the ROS 2 ecosystem. This way, the following commands are integrating into the ROS 2 commands:
Using this command launch a LLM from a YAML file. The configuration of the YAML is used to launch the LLM in the same way as using a regular launch file. Here is an example of how to use it:
$ ros2 llama launch ~/ros2_ws/src/llama_ros/llama_bringup/params/StableLM-Zephyr.yaml
Using this command send a prompt to a launched LLM. The command uses a string, which is the prompt and has the following arguments:
- (
-r
,--reset
): Whether to reset the LLM before prompting - (
-t
,--temp
): The temperature value - (
--image-url
): Image url to sent to a VLM
Here is an example of how to use it:
$ ros2 llama prompt "Do you know ROS 2?" -t 0.0
First of all, you need to create a launch file to use llama_ros or llava_ros. This launch file will contain the main parameters to download the model from HuggingFace and configure it. Take a look at the following examples and the predefined launch files.
Click to expand
from launch import LaunchDescription
from llama_bringup.utils import create_llama_launch
def generate_launch_description():
return LaunchDescription([
create_llama_launch(
n_ctx=2048, # context of the LLM in tokens
n_batch=8, # batch size in tokens
n_gpu_layers=0, # layers to load in GPU
n_threads=1, # threads
n_predict=2048, # max tokens, -1 == inf
model_repo="TheBloke/Marcoroni-7B-v3-GGUF", # Hugging Face repo
model_filename="marcoroni-7b-v3.Q4_K_M.gguf", # model file in repo
system_prompt_type="alpaca" # system prompt type
)
])
$ ros2 launch llama_bringup marcoroni.launch.py
Click to expand
n_ctx: 2048 # context of the LLM in tokens
n_batch: 8 # batch size in tokens
n_gpu_layers: 0 # layers to load in GPU
n_threads: 1 # threads
n_predict: 2048 # max tokens, -1 == inf
model_repo: "cstr/Spaetzle-v60-7b-GGUF" # Hugging Face repo
model_filename: "Spaetzle-v60-7b-q4-k-m.gguf" # model file in repo
system_prompt_type: "Alpaca" # system prompt type
import os
from launch import LaunchDescription
from llama_bringup.utils import create_llama_launch_from_yaml
from ament_index_python.packages import get_package_share_directory
def generate_launch_description():
return LaunchDescription([
create_llama_launch_from_yaml(os.path.join(
get_package_share_directory("llama_bringup"), "params", "Spaetzle.yaml"))
])
$ ros2 launch llama_bringup spaetzle.launch.py
Click to expand
from launch import LaunchDescription
from llama_bringup.utils import create_llama_launch
def generate_launch_description():
return LaunchDescription([
create_llama_launch(
use_llava=True, # enable llava
n_ctx=8192, # context of the LLM in tokens, use a huge context size to load images
n_batch=512, # batch size in tokens
n_gpu_layers=33, # layers to load in GPU
n_threads=1, # threads
n_predict=8192, # max tokens, -1 == inf
model_repo="cjpais/llava-1.6-mistral-7b-gguf", # Hugging Face repo
model_filename="llava-v1.6-mistral-7b.Q4_K_M.gguf", # model file in repo
mmproj_repo="cjpais/llava-1.6-mistral-7b-gguf", # Hugging Face repo
mmproj_filename="mmproj-model-f16.gguf", # mmproj file in repo
system_prompt_type="mistral" # system prompt type
)
])
$ ros2 launch llama_bringup llava.launch.py
Click to expand
use_llava: True # enable llava
n_ctx: 8192 # context of the LLM in tokens use a huge context size to load images
n_batch: 512 # batch size in tokens
n_gpu_layers: 33 # layers to load in GPU
n_threads: 1 # threads
n_predict: 8192 # max tokens -1 : : inf
model_repo: "cjpais/llava-1.6-mistral-7b-gguf" # Hugging Face repo
model_filename: "llava-v1.6-mistral-7b.Q4_K_M.gguf" # model file in repo
mmproj_repo: "cjpais/llava-1.6-mistral-7b-gguf" # Hugging Face repo
mmproj_filename: "mmproj-model-f16.gguf" # mmproj file in repo
system_prompt_type: "mistral" # system prompt type
def generate_launch_description():
return LaunchDescription([
create_llama_launch_from_yaml(os.path.join(
get_package_share_directory("llama_bringup"),
"params", "llava-1.6-mistral-7b-gguf.yaml"))
])
$ ros2 launch llama_bringup llava.launch.py
You can use LoRA adapters when launching LLMs. Using llama.cpp features, you can load multiple adapters choosing the scale to apply for each adapter. Here you have an example of using LoRA adapters with Phi-3. You can lis the
LoRAs using the /llama/list_loras
service and modify their scales values by using the /llama/update_loras
service. A scale value of 0.0 means not using that LoRA.
Click to expand
n_ctx: 2048
n_batch: 8
n_gpu_layers: 0
n_threads: 1
n_predict: 2048
model_repo: "bartowski/Phi-3.5-mini-instruct-GGUF"
model_filename: "Phi-3.5-mini-instruct-Q4_K_M.gguf"
lora_adapters:
- repo: "zhhan/adapter-Phi-3-mini-4k-instruct_code_writing"
filename: "Phi-3-mini-4k-instruct-adaptor-f16-code_writer.gguf"
scale: 0.5
- repo: "zhhan/adapter-Phi-3-mini-4k-instruct_summarization"
filename: "Phi-3-mini-4k-instruct-adaptor-f16-summarization.gguf"
scale: 0.5
system_prompt_type: "Phi-3"
Both llama_ros and llava_ros provide ROS 2 interfaces to access the main functionalities of the models. Here you have some examples of how to use them inside ROS 2 nodes. Moreover, take a look to the llama_demo_node.py and llava_demo_node.py demos.
Click to expand
from rclpy.node import Node
from llama_msgs.srv import Tokenize
class ExampleNode(Node):
def __init__(self) -> None:
super().__init__("example_node")
# create the client
self.srv_client = self.create_client(Tokenize, "/llama/tokenize")
# create the request
req = Tokenize.Request()
req.prompt = "Example text"
# call the tokenize service
self.srv_client.wait_for_service()
res = self.srv_client.call(req)
tokens = res.tokens
Click to expand
Remember to launch llama_ros with embedding set to true to be able of generating embeddings with your LLM.
from rclpy.node import Node
from llama_msgs.srv import Embeddings
class ExampleNode(Node):
def __init__(self) -> None:
super().__init__("example_node")
# create the client
self.srv_client = self.create_client(Embeddings, "/llama/generate_embeddings")
# create the request
req = Embeddings.Request()
req.prompt = "Example text"
req.normalize = True
# call the embedding service
self.srv_client.wait_for_service()
res = self.srv_client.call(req)
embeddings = res.embeddings
Click to expand
import rclpy
from rclpy.node import Node
from rclpy.action import ActionClient
from llama_msgs.action import GenerateResponse
class ExampleNode(Node):
def __init__(self) -> None:
super().__init__("example_node")
# create the client
self.action_client = ActionClient(
self, GenerateResponse, "/llama/generate_response")
# create the goal and set the sampling config
goal = GenerateResponse.Goal()
goal.prompt = self.prompt
goal.sampling_config.temp = 0.2
# wait for the server and send the goal
self.action_client.wait_for_server()
send_goal_future = self.action_client.send_goal_async(
goal)
# wait for the server
rclpy.spin_until_future_complete(self, send_goal_future)
get_result_future = send_goal_future.result().get_result_async()
# wait again and take the result
rclpy.spin_until_future_complete(self, get_result_future)
result: GenerateResponse.Result = get_result_future.result().result
Click to expand
import cv2
from cv_bridge import CvBridge
import rclpy
from rclpy.node import Node
from rclpy.action import ActionClient
from llama_msgs.action import GenerateResponse
class ExampleNode(Node):
def __init__(self) -> None:
super().__init__("example_node")
# create a cv bridge for the image
self.cv_bridge = CvBridge()
# create the client
self.action_client = ActionClient(
self, GenerateResponse, "/llama/generate_response")
# create the goal and set the sampling config
goal = GenerateResponse.Goal()
goal.prompt = self.prompt
goal.sampling_config.temp = 0.2
# add your image to the goal
image = cv2.imread("/path/to/your/image", cv2.IMREAD_COLOR)
goal.image = self.cv_bridge.cv2_to_imgmsg(image)
# wait for the server and send the goal
self.action_client.wait_for_server()
send_goal_future = self.action_client.send_goal_async(
goal)
# wait for the server
rclpy.spin_until_future_complete(self, send_goal_future)
get_result_future = send_goal_future.result().get_result_async()
# wait again and take the result
rclpy.spin_until_future_complete(self, get_result_future)
result: GenerateResponse.Result = get_result_future.result().result
There is a llama_ros integration for LangChain. Thus, prompt engineering techniques could be applied. Here you have an example to use it.
Click to expand
import rclpy
from llama_ros.langchain import LlamaROS
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
rclpy.init()
# create the llama_ros llm for langchain
llm = LlamaROS()
# create a prompt template
prompt_template = "tell me a joke about {topic}"
prompt = PromptTemplate(
input_variables=["topic"],
template=prompt_template
)
# create a chain with the llm and the prompt template
chain = prompt | llm | StrOutputParser()
# run the chain
text = chain.invoke({"topic": "bears"})
print(text)
rclpy.shutdown()
Click to expand
import rclpy
from llama_ros.langchain import LlamaROS
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
rclpy.init()
# create the llama_ros llm for langchain
llm = LlamaROS()
# create a prompt template
prompt_template = "tell me a joke about {topic}"
prompt = PromptTemplate(
input_variables=["topic"],
template=prompt_template
)
# create a chain with the llm and the prompt template
chain = prompt | llm | StrOutputParser()
# run the chain
for c in chain.stream({"topic": "bears"}):
print(c, flush=True, end="")
rclpy.shutdown()
Click to expand
import rclpy
from llama_ros.langchain import LlamaROS
rclpy.init()
# create the llama_ros llm for langchain
llm = LlamaROS()
# bind the url_image
llm = llm.bind(image_url=image_url).stream("Describe the image")
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
# run the llm
for c in llm:
print(c, flush=True, end="")
rclpy.shutdown()
Click to expand
import rclpy
from langchain_community.vectorstores import Chroma
from llama_ros.langchain import LlamaROSEmbeddings
rclpy.init()
# create the llama_ros embeddings for lanchain
embeddings = LlamaROSEmbeddings()
# create a vector database and assign it
db = Chroma(embedding_function=embeddings)
# create the retriever
retriever = db.as_retriever(search_kwargs={"k": 5})
# add your texts
db.add_texts(texts=["your_texts"])
# retrieve documents
docuemnts = retriever.get_relevant_documents("your_query")
print(docuemnts)
rclpy.shutdown()
$ ros2 launch llama_bringup spaetzle.launch.py
$ ros2 run llama_demos llama_demo_node --ros-args -p prompt:="your prompt"
llama_ros_gpu_new_1.mp4
$ ros2 launch llama_bringup minicpm-2.6.launch.py
$ ros2 run llama_demos llava_demo_node --ros-args -p prompt:="your prompt" -p image_url:="url of the image" -p use_image:="whether to send the image"
frieren_1.mp4
$ ros2 launch llama_bringup minicpm-2.6.launch.py
$ ros2 run llama_demos chatllama_demo_node