hololeo

#!/bin/sh
# rename-pictures.sh
# Author: Justine Tunney <[email protected]>
# License: Apache 2.0
#
# This shell script can be used to ensure all the images in a folder
# have good descriptive filenames that are written in English. It's
# based on the Mistral 7b and LLaVA v1.5 models.
#
# For example, the following command:

hololeo

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

hololeo

from litellm import completion

def stream_response(response):
    for chunk in response:
        chunk_content = chunk['choices'][0]['delta'].get('content', '')
        if chunk_content:
            yield chunk_content

def main():
    response = completion(

hololeo

from litellm import completion
from datetime import datetime
import os
import glob
# importlib.reload(chatbot); from chatbot import Chatbot; hacs = Chatbot()

class Chatbot:
    def __init__(self, model="ollama/llama3.2:latest", api_base="http://localhost:11434"):
        self.model = model
        self.api_base = api_base

hololeo

# taken from Ollama discord users

Ollama Cheat Sheet: Use Cases and Commands
Here is a cheat sheet of Ollama commands and their corresponding use cases, based on the provided sources and our conversation history.
Basic Commands
●
ollama run [model_name]: This command starts an interactive session with a specific model. For example, ollama run llama2 starts a conversation with the Llama 2 7b model.
●
ollama pull [model_name]: Use this to download a model from the Ollama registry. Example: ollama pull llama2-uncensored downloads the uncensored variant of Llama 2.
●

hololeo

<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times