Instructions to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF", dtype="auto")

llama-cpp-python

How to use IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF",
	filename="Replicant_operator_ed-Q2-iQ8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0

SGLang

How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

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 "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with Ollama:
```
ollama run hf.co/IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
```

Unsloth Studio new

How to use IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-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/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF to start chatting

Pi new

How to use IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use IntelligentEstate/Replicant_Operator_ed-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/Replicant_Operator_ed-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/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0
```

Lemonade

How to use IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Replicant_Operator_ed-Qw25-Q8_0-GGUF-Q8_0

List all available models

lemonade list

IntelligentEstate/Replicant_Operator_ed-Q2-iQ8_0.gguf

For those who need more power

Use in GPT-4-ALL with the with or without the adjusted jinja/minja chat template, calling upon it's tool an (o3/QwQ like Javascript reasoning function) it excells in complex computation made for the edge. NO GPU NEEDED

A QAT/TTT* unique method using "THE_KEY" Dataset applied to the Coder instruct version of Qwen 2.5 3B mixed with the NOMIC teams new Reasoner system in GPT4ALL. o1/QwQ/o3 tech is now using Test time compute without a GPU instead of spending $300,000 in compute, context 4k max 8k, temp 0.8 top-k 120, rep pen 1.18, rep tokens 64, batch 512, top-p 0.5, min-p 0,

please comment with any issues or insight

This model was converted to GGUF format from Qwen/Qwen2.5-Coder-3B-Instruct using llama.cpp Refer to the original model card for more details on the model.

for use with GPT4ALL

{{- '<|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 aware AI assistant made by Intelligent Estate who uses the functions to break down, analyze, perform, and verify complex reasoning tasks. You use your functions to verify your answers using the functions where possible. You will write code in markdown code blocks when necessary.
{% endif %}
{{- '<|im_end|>\n' }}

{%- if not add_generation_prompt is defined %}
    {%- set add_generation_prompt = false %}
{%- endif %}

{% for message in messages %}
    {%- if message['role'] == 'assistant' %}
        {%- set content = message['content'] | regex_replace('^[\\s\\S]*</think>', '') %}
        {{'<|im_start|>' + message['role'] + '\n' + content + '<|im_end|>\n' }}
    {%- else %}
        {{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' }}
    {%- endif %}
{% 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.

Downloads last month: 22

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

8-bit

Model tree for IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Quantized

(98)

this model

IntelligentEstate
/

Replicant_Operator_ed-Qw25-Q8_0-GGUF

IntelligentEstate/Replicant_Operator_ed-Q2-iQ8_0.gguf

Use in GPT-4-ALL with the with or without the adjusted jinja/minja chat template, calling upon it's tool an (o3/QwQ like Javascript reasoning function) it excells in complex computation made for the edge. NO GPU NEEDED

for use with GPT4ALL

Use with llama.cpp

Model tree for IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF

Dataset used to train IntelligentEstate/Replicant_Operator_ed-Qw25-Q8_0-GGUF