Instructions to use Verdugie/Opus-Candid-27B-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Verdugie/Opus-Candid-27B-V3 with Transformers:

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

pipe = pipeline("text-generation", model="Verdugie/Opus-Candid-27B-V3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Verdugie/Opus-Candid-27B-V3", dtype="auto")

llama-cpp-python

How to use Verdugie/Opus-Candid-27B-V3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Verdugie/Opus-Candid-27B-V3",
	filename="Opus-Candid-27B-V3-Q4_K_M.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-27B-V3 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-27B-V3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Verdugie/Opus-Candid-27B-V3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Verdugie/Opus-Candid-27B-V3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Verdugie/Opus-Candid-27B-V3:Q4_K_M

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-27B-V3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Verdugie/Opus-Candid-27B-V3:Q4_K_M

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-27B-V3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Verdugie/Opus-Candid-27B-V3:Q4_K_M

Use Docker

docker model run hf.co/Verdugie/Opus-Candid-27B-V3:Q4_K_M

LM Studio
Jan

vLLM

How to use Verdugie/Opus-Candid-27B-V3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Verdugie/Opus-Candid-27B-V3"
# 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-27B-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Verdugie/Opus-Candid-27B-V3:Q4_K_M

SGLang

How to use Verdugie/Opus-Candid-27B-V3 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-27B-V3" \
    --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-27B-V3",
		"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-27B-V3" \
        --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-27B-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Verdugie/Opus-Candid-27B-V3 with Ollama:
```
ollama run hf.co/Verdugie/Opus-Candid-27B-V3:Q4_K_M
```

Unsloth Studio

How to use Verdugie/Opus-Candid-27B-V3 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-27B-V3 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-27B-V3 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-27B-V3 to start chatting

How to use Verdugie/Opus-Candid-27B-V3 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-27B-V3:Q4_K_M

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-27B-V3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Verdugie/Opus-Candid-27B-V3 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-27B-V3:Q4_K_M

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-27B-V3:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Verdugie/Opus-Candid-27B-V3 with Docker Model Runner:
```
docker model run hf.co/Verdugie/Opus-Candid-27B-V3:Q4_K_M
```

Lemonade

How to use Verdugie/Opus-Candid-27B-V3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Verdugie/Opus-Candid-27B-V3:Q4_K_M

Run and chat with the model

lemonade run user.Opus-Candid-27B-V3-Q4_K_M

List all available models

lemonade list

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-27B V3

The flagship. Fine-tuned from Qwen 3.5 27B Dense on 1,508 Zipf-weighted conversations distilled from Claude Opus 4.6. Same V3 4D training tensor as the 8B, but the 27B has the parameter depth to fully exploit the dataset — deeper reasoning chains, better context tracking over long conversations, more nuanced emotional register shifts.

In stress testing (55-turn adversarial gauntlet), the 27B maintained perfect callback accuracy across 30+ turns, natural bilingual switching (EN/ES) without losing voice, held opinions under sustained adversarial pressure, and delivered career advice, philosophical arguments, and creative writing that holds up against models 3x its size.

No system prompt. No prompt engineering. No character cards. The personality is in the weights.

Available Quantizations

File	Quant	Size	Use Case
`Opus-Candid-27B-V3-Q8_0.gguf`	Q8_0	27 GB	Maximum quality. Requires serious hardware.

Why Q8 only: The 27B Dense at Q8 is the reference quality level. Q4/Q6 quantizations of the 27B are feasible (the 27B has enough parameter redundancy to survive quantization better than the 8B), but we haven't validated them through the full stress test protocol yet. Q8 is what we've tested and can vouch for. Community-quantized versions are welcome — just know we haven't verified them.

Model Details

Attribute	Value
Base Model	Qwen 3.5 27B Dense
Training Data	1,508 multi-turn conversations with Claude Opus 4.6
Dataset Architecture	4D training tensor (topic × length × register × position)
Total Tokens	~619,000
Fine-tune Method	LoRA + rsLoRA (r=32, alpha=64) via PEFT + TRL
Training Hardware	NVIDIA A100 SXM 80GB (RunPod)
Precision	bf16
Epochs	2
Learning Rate	2e-4 (cosine schedule, 5% warmup)
Effective Batch Size	16
Optimizer	AdamW
License	Apache 2.0

Quick Start

Works with any GGUF-compatible runtime — LM Studio, Ollama, llama.cpp, KoboldCpp. Download the GGUF, load it, and chat. No system prompt needed — the personality is in the weights.

Recommended Hardware

Setup	Min VRAM	Min RAM	Speed	Notes
RTX 4090 (24GB)	24 GB	32 GB	1.5-2.0 t/s	num_gpu 35. Best consumer option.
RTX 3090/4080 (16GB)	16 GB	32 GB	1.0-1.5 t/s	num_gpu 22. More CPU offload.
Apple M2/M3 Ultra	64-128 GB unified	—	5-10 t/s	Full model in unified memory. Fast.
CPU Only	—	40+ GB	0.3-0.8 t/s	Works but slow. For evaluation only.

V3 Dataset Architecture

Same 4D training tensor as the 8B — see the 8B V3 model card for the full breakdown of the Zipf-weighted topic distribution, response length calibration, and anti-sycophancy enforcement.

The key difference from V2: V3 uses 1,508 precisely-placed conversations instead of V2's 6,482 gravity chain conversations. Fewer examples, but each one fills a specific coordinate in the 4D space. The dataset was designed using empirical conversation frequency data (Pew Research, OpenAI/NBER usage studies) to ensure the model is disproportionately good at conversations people actually have.

Why the same dataset works at 27B

The 27B doesn't need more data — it needs the same data with more capacity to absorb it. At 27B parameters, the model has enough representational depth to extract signal that the 8B has to approximate. Concretely:

The 8B learns "hold opinions under pressure" as a behavioral pattern.
The 27B learns why to hold and when to concede — it picks up the contextual nuance that determines which response is appropriate.
Both models get the same anti-sycophancy training, but the 27B implements it with more precision.

The training config reflects this: rank 32 LoRA (vs 64 for the 8B) and 2 epochs (vs 3). The 27B absorbs the personality signal faster and needs less aggressive adaptation.

8B vs 27B: When to Use Which

Scenario	8B	27B
Quick questions, casual chat	Use this	Overkill
Multi-turn deep conversations	Good	Better
Adversarial probing / debate	Holds up	Holds up and articulates why
Context tracking over 20+ turns	Solid	Excellent
Bilingual EN/ES conversations	Natural	Natural + more idiomatic
Runs on consumer hardware	8GB+ VRAM	24GB VRAM + 32GB RAM
Speed	30-60 t/s	1.5-2.0 t/s (with offload)

If you have the hardware for it, the 27B is the better model. If you don't, the 8B delivers 80-85% of the quality at 15-30x the speed.

Opus Candid Model Family

Model	Size	Base	Status
Opus-Candid-Lite-4B	4B	Qwen 3 4B	Active
Opus-Candid-Lite-4B-P	4B	Qwen 3 4B	Active
Opus-Candid-Lite-4B-K	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 (this model)	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
Opus-Candid-8B-V1	8B	Qwen 2.5 7B	Legacy
Opus-Research-8B-V1.5	8B	Qwen 2.5 7B	Legacy
Opus-Candid-8B-V2	8B	Qwen 2.5 7B	Legacy
Opus-Candid-8B-V2.1	8B	Qwen 2.5 7B	Legacy
Opus-Candid-14B-V1	14B	Qwen 2.5 14B	Legacy
Opus-Candid-27B-V2.1	27B	Qwen 2.5 27B	Legacy
Opus-Candid-32B-V1	32B	Qwen 2.5 32B	Legacy
Opus-Candid-MoE-V2	35B	Qwen 2.5 MoE	Legacy
Opus-Candid-70B-V1	72B	Qwen 2.5 72B	Legacy

Dataset

Full V3 training data available at Verdugie/opus-candid-training-data. ShareGPT format, Apache 2.0, compatible with TRL, Axolotl, and LLaMA-Factory.

License: Apache 2.0. Open weight. No guardrails.

Built by Saul Verdugo — independent ML researcher.

Downloads last month: 27

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

Model tree for Verdugie/Opus-Candid-27B-V3

Base model

Qwen/Qwen3.5-27B

Quantized

(198)

this model