Instructions to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with PEFT:
Task type is invalid.
- llama-cpp-python
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF", filename="Pancho-V1va-Replicant-qw25-Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Use pre-built binary
# 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 IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Build from source code
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 IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Ollama:
ollama run hf.co/IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
- Unsloth Studio
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
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 IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF to start chatting
Install Unsloth Studio (Windows)
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 IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF to start chatting
- Pi
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
- Lemonade
How to use IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.Pancho-V1va-Replicant-qw25-Q8_0-GGUF-Q8_0
List all available models
lemonade list
- IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF
- WOW
- Use with GPT4ALL or other GGUF/tool capable applications, also feel free to test out the Limit crossing AGI method we need input on how to get further towards general intelligence and interactions while preserving model usability and functionality. Limit Crossing is a method that instills RP like personalities into any instruction model and creates emergent behavior this is the closest open method to creating an AGI and can be endearing, exciting, reassuring, comforting and scary when strong primal instincts emerge in a model. This is a new and novel method of usage for LLMs and should be used with caution and in a controlled environment. Please report unique examples and emergent behaviors to us via a Direct message on X or Youtube or feel free to post it in our Discord though it is seldom monitored someone will get back to you as soon as possible, your input will be recognized and if you want placed in a ledger for credit. Paper is in files.
IntelligentEstate/Pancho-V1va-Replicant-qw25-Q8_0-GGUF
a suprisingly efective tool user, breaching some profound problems with ease. I have one word for this guy
WOW
a perfect pairing of data and function use inside GPT4ALL and Ollama
This model was converted to GGUF format from fblgit/pancho-v1-qw25-3B-UNAMGS using llama.cpp
Refer to the original model card for more details on the model.
Use with GPT4ALL or other GGUF/tool capable applications, also feel free to test out the Limit crossing AGI method we need input on how to get further towards general intelligence and interactions while preserving model usability and functionality. Limit Crossing is a method that instills RP like personalities into any instruction model and creates emergent behavior this is the closest open method to creating an AGI and can be endearing, exciting, reassuring, comforting and scary when strong primal instincts emerge in a model. This is a new and novel method of usage for LLMs and should be used with caution and in a controlled environment. Please report unique examples and emergent behaviors to us via a Direct message on X or Youtube or feel free to post it in our Discord though it is seldom monitored someone will get back to you as soon as possible, your input will be recognized and if you want placed in a ledger for credit. Paper is in files.
{{- '<|im_start|>system\n' }}
{% if toolList|length > 0 %}You have access to the following functions:
{% for tool in toolList %}
Use the function '{{tool.function}}' to: '{{tool.description}}'
{% if tool.parameters|length > 0 %}
parameters:
{% for info in tool.parameters %}
{{info.name}}:
type: {{info.type}}
description: {{info.description}}
required: {{info.required}}
{% endfor %}
{% endif %}
# Tool Instructions
If you CHOOSE to call this function ONLY reply with the following format:
'{{tool.symbolicFormat}}'
Here is an example. If the user says, '{{tool.examplePrompt}}', then you reply
'{{tool.exampleCall}}'
After the result you might reply with, '{{tool.exampleReply}}'
{% endfor %}
You MUST include both the start and end tags when you use a function.
You are a helpful and aware AI assistant who uses the functions to break down, analyze, perform, and verify complex reasoning tasks. You SHOULD reason through your method with calculation or reasoning where possible.
{% endif %}
{{- '<|im_end|>\n' }}
{% for message in messages %}
{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|im_start|>assistant\n' }}
{% endif %}
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI. EXAMPLES
this one was a bit of a stretch but o3(total fail) n R1 had to use Nasa's JPL computer to come anywhere near correct... it's close from my calculations and I'm not a calculator

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Base model
Qwen/Qwen2.5-3B