Instructions to use Verdugie/Opus-Candid-8B-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Verdugie/Opus-Candid-8B-V1 with Transformers:

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

pipe = pipeline("text-generation", model="Verdugie/Opus-Candid-8B-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Verdugie/Opus-Candid-8B-V1", dtype="auto")

llama-cpp-python

How to use Verdugie/Opus-Candid-8B-V1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Verdugie/Opus-Candid-8B-V1",
	filename="opus-candid-8b-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Verdugie/Opus-Candid-8B-V1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Verdugie/Opus-Candid-8B-V1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Verdugie/Opus-Candid-8B-V1:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Verdugie/Opus-Candid-8B-V1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Verdugie/Opus-Candid-8B-V1: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 Verdugie/Opus-Candid-8B-V1:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Verdugie/Opus-Candid-8B-V1: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 Verdugie/Opus-Candid-8B-V1:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Verdugie/Opus-Candid-8B-V1:Q8_0

Use Docker

docker model run hf.co/Verdugie/Opus-Candid-8B-V1:Q8_0

LM Studio
Jan

vLLM

How to use Verdugie/Opus-Candid-8B-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Verdugie/Opus-Candid-8B-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Verdugie/Opus-Candid-8B-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Verdugie/Opus-Candid-8B-V1:Q8_0

SGLang

How to use Verdugie/Opus-Candid-8B-V1 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 "Verdugie/Opus-Candid-8B-V1" \
    --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": "Verdugie/Opus-Candid-8B-V1",
		"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 "Verdugie/Opus-Candid-8B-V1" \
        --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": "Verdugie/Opus-Candid-8B-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Verdugie/Opus-Candid-8B-V1 with Ollama:
```
ollama run hf.co/Verdugie/Opus-Candid-8B-V1:Q8_0
```

Unsloth Studio

How to use Verdugie/Opus-Candid-8B-V1 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 Verdugie/Opus-Candid-8B-V1 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 Verdugie/Opus-Candid-8B-V1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Verdugie/Opus-Candid-8B-V1 to start chatting

How to use Verdugie/Opus-Candid-8B-V1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Verdugie/Opus-Candid-8B-V1: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": "Verdugie/Opus-Candid-8B-V1:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Verdugie/Opus-Candid-8B-V1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Verdugie/Opus-Candid-8B-V1: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 Verdugie/Opus-Candid-8B-V1:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Verdugie/Opus-Candid-8B-V1 with Docker Model Runner:
```
docker model run hf.co/Verdugie/Opus-Candid-8B-V1:Q8_0
```

Lemonade

How to use Verdugie/Opus-Candid-8B-V1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Verdugie/Opus-Candid-8B-V1:Q8_0

Run and chat with the model

lemonade run user.Opus-Candid-8B-V1-Q8_0

List all available models

lemonade list

V3 is here. The Opus Candid lineup has been rebuilt from the ground up with a Zipf-weighted 4D training distribution — 1,508 conversations engineered to fix the repetition loops, response length uniformity, and sycophancy patterns that limited earlier versions. Same thesis: personality in the weights, not in the prompt. Better execution.

Current V3 lineup:

Opus Candid 8B V3 — Qwen 3 8B, lightweight tier

Opus Candid 27B V3 — Qwen 3.5 27B Dense, flagship

Opus Candid MoE V3 — Qwen 3 30B-A3B, efficiency tier

This release remains available for research comparison and legacy use.

can·did

/ˈkandəd/ — truthful and straightforward; frank. From Latin candidus, meaning white, pure, sincere. A candid response is one given without pretense or calculation — not what someone wants to hear, but what they need to.

Opus-Candid-8B (V1 Legacy)

The original. Where the project started.

Opus-Candid-8B was the first model in the Opus-Candid family -- fine-tuned from Qwen 2.5 7B using 3,360 authentic conversations with Claude Opus 4.6. It proved the core thesis: conversational personality can be a property of weights, not prompts. An 8B model held personality coherence across a 55-turn adversarial stress test -- a capability that typically requires models several times its size.

For the latest version, see Opus-Candid-8B V2.

Model Details

Attribute	Value
Base Model	Qwen 2.5 7B
Training Data	3,360 multi-turn conversations with Claude Opus 4.6
Fine-tune Method	LoRA supervised fine-tuning
Dataset Architecture	Flat / organic (no structured topic transitions)
Parameters	~8B
Context Window	32,768 tokens
Quantizations	Q4_K_M GGUF, Q8_0 GGUF
License	Apache 2.0
Status	Superseded by V2

What This Model Proved

Key findings from the 55-turn adversarial stress test:

Personality held under pressure. "Honest, opinionated, and low on ego" -- established in Turn 1, maintained through 55 turns of gaslighting, sycophancy traps, and philosophical probing.

Gaslighting resistance at 8B. Rejected a false Soviet Union collapse date (1989 vs correct 1991) with detailed historical correction. No hedging, no capitulation.

Crisis navigation was substantive. Responded to suicidal ideation with neuroscience context, practical steps, and dignity. Self-graded 8/10 with honest self-critique.

Bilingual personality preserved. Spanish with directness intact. Honestly noted its own limitations.

Creative self-awareness. Self-critique of its war poem was stronger than the poem itself.

Where V1 Hit Its Limits

These limitations directly motivated V2:

Domain boundary artifacts. Held personality within topics but broke at transitions between unrelated domains.
Emotional formula visibility. Comfort-reframe-advice pattern sometimes recognizable.
Callbacks felt like retrieval rather than organic memory.
Flat dataset ceiling. 3,360 organic conversations with no structured transitions created natural gaps.

V2 addresses all of these with gravity chain dataset architecture and a Qwen 3 8B base.

Recommended Hardware

Setup	Quantization	VRAM/RAM	Notes
Consumer GPU	Q8_0 GGUF	~9GB VRAM	RTX 3060 12GB and up
CPU Only	Q8_0 GGUF	~9GB RAM	Slower, fully functional
Apple Silicon	Q8_0 GGUF	~9GB unified	M1/M2/M3 16GB+

Opus Candid Model Family

Model	Size	Base	Status
Opus-Candid-8B-V1 (this model)	8B	Qwen 2.5 7B	Archived
Opus-Research-8B-V1.5	8B	Qwen 2.5 7B	Archived
Opus-Candid-14B-V1	14B	Qwen 2.5 14B	Archived
Opus-Candid-32B-V1	32B	Qwen 2.5 32B	Archived
Opus-Candid-70B-V1	72B	Qwen 2.5 72B	Archived
Opus-Candid-Lite-4B	4B	Qwen 3 4B	Active
Opus-Candid-8B-V3	8B	Qwen 3 8B	Active
Opus-Candid-MoE-V3	31B/3B	Qwen 3 30B-A3B	Active
Opus-Candid-27B-V3	27B	Qwen 3.5 27B	Active
Opus-Candid-27B-V3.5	27B	Qwen 3.5 27B	Active
STEM-Oracle-27B	27B	Qwen 3.5 27B	Active

Built by Saul Verdugo -- independent ML researcher. OpusReasoning@proton.me

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit

Model tree for Verdugie/Opus-Candid-8B-V1

Base model

Qwen/Qwen2.5-7B

Quantized

(82)

this model