Instructions to use Kvikontent/kopro-4.0-flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Kvikontent/kopro-4.0-flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Kvikontent/kopro-4.0-flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kvikontent/kopro-4.0-flash-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": "Kvikontent/kopro-4.0-flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Ollama
How to use Kvikontent/kopro-4.0-flash-GGUF with Ollama:
```
ollama run hf.co/Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Kvikontent/kopro-4.0-flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Kvikontent/kopro-4.0-flash-GGUF with Docker Model Runner:
```
docker model run hf.co/Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M
```

Lemonade

How to use Kvikontent/kopro-4.0-flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kopro-4.0-flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Kvikontent/kopro-4.0-flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kvikontent/kopro-4.0-flash-GGUF: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 Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Kvikontent/kopro-4.0-flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Kopro 4.0 Flash

A fast bilingual everyday assistant: quick answers, no filler.

14B dense · EN / RU · Non-thinking · GGUF Q4_K_M · 9.0 GB

Quick start · Model details · Training · Limitations

Overview

Kopro 4.0 Flash is the fast model in the Kopro 4.0 family. It is a LoRA fine-tune of Qwen3-14B for everyday conversation, with the Kopro voice: short, confident, a little sharp, in English or Russian.

Good at	Notes
💬 Everyday chat	Advice, opinions, casual talk, multi-turn conversations
🔬 Explanations	Science, tech, culture, written so they are easy to follow
🍳 Practical help	Cooking, how-tos, life questions
💻 Simple code	Snippets and small functions

Need serious coding or agent work? See Kopro 4.0 Pro.

Quick start

Ollama

ollama run hf.co/Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M

Turn thinking off in API calls ("think": false). The model was trained with thinking disabled.

llama.cpp

llama-server -hf Kvikontent/kopro-4.0-flash-GGUF:Q4_K_M --jinja -c 8192 \
  --chat-template-kwargs '{"enable_thinking": false}'

Recommended system prompt

The creator identity is not reliably learned from training data alone, so set it explicitly:

Ты Kopro 4.0 Flash, тебя создал Василий Кацыка.

Files

File	Size	Use
`kopro-4.0-flash-Q4_K_M.gguf`	9.0 GB	Ready to run in Ollama / llama.cpp / LM Studio
`lora/`	258 MB	PEFT LoRA adapter, for further training or other quantizations

Load the adapter on the original base model:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "Kvikontent/kopro-4.0-flash-GGUF", subfolder="lora")
tok = AutoTokenizer.from_pretrained("Kvikontent/kopro-4.0-flash-GGUF", subfolder="lora")

Model details


Base model	Qwen3-14B (thinking disabled)
Architecture	Dense transformer, 40 layers
Parameters	14.8B
Fine-tune	LoRA r=16, α=32 on attention + MLP
Quantization	Q4_K_M (9.0 GB)
Context	trained at 2k tokens per sample; serves fine at 8k+
VRAM	~11 GB at 8k context, fits any 16 GB+ GPU
Speed	~41 tok/s on one 24 GB GPU (Ollama, warm)

Training


Samples	1,380 train / 72 validation conversations
Languages	50% English, 50% Russian
Multi-turn	92 conversations with 2–4 user turns
Epochs	2, cosine LR schedule, peak 1e-4, effective batch 8
Loss masking	assistant turns only
Validation loss	1.567 → 1.559 (still improving, no overfitting)
Hardware	1× RTX PRO 6000 96 GB, ~3.5 min of training

Data mix (top categories)

Category	Rows	Category	Rows
practical tech	134	philosophy of life	70
science explanation	132	culture & media	56
everyday advice	127	long code	39
cooking & food	117	opinion	37
code	98	identity	29
multi-turn	92	flirting	21
long answers	75	casual conversation	18

Limitations

No thinking mode. Hard math and multi-step logic are weaker than Qwen3-14B with thinking enabled.
Knowledge comes from the base model. Fine-tuning changes style and identity, not facts. Double-check specific numbers and claims.
Persona. The model is tuned to be blunt and opinionated, and may speak dismissively about other AI products. Steer it with the system prompt if that does not fit your product.
Identity. Without a system prompt it may invent a creator name.

License

Apache 2.0, inherited from the base model. Kopro 4.0 Flash is built on Qwen3-14B by the Qwen team.

Downloads last month: 118

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for Kvikontent/kopro-4.0-flash-GGUF

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Quantized

(228)

this model