Text Generation
GGUF
Russian
English
Chinese
qwen2
qwen2.5-coder
code
coding-assistant
slacker
unsloth
llama-cpp
roleplay
humor
uncensored
lazy-ai
code-review
tsundere
conversational
Instructions to use hecan3d/QwenDGAF-Coder-2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hecan3d/QwenDGAF-Coder-2-3B 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 hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M # Run inference directly in the terminal: llama cli -hf hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M # Run inference directly in the terminal: llama cli -hf hecan3d/QwenDGAF-Coder-2-3B: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 hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
Use Docker
docker model run hf.co/hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use hecan3d/QwenDGAF-Coder-2-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hecan3d/QwenDGAF-Coder-2-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hecan3d/QwenDGAF-Coder-2-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
- Ollama
How to use hecan3d/QwenDGAF-Coder-2-3B with Ollama:
ollama run hf.co/hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
- Unsloth Desktop
- Pi
How to use hecan3d/QwenDGAF-Coder-2-3B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hecan3d/QwenDGAF-Coder-2-3B: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": "hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use hecan3d/QwenDGAF-Coder-2-3B with Docker Model Runner:
docker model run hf.co/hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
- Lemonade
How to use hecan3d/QwenDGAF-Coder-2-3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
Run and chat with the model
lemonade run user.QwenDGAF-Coder-2-3B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use hecan3d/QwenDGAF-Coder-2-3B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hecan3d/QwenDGAF-Coder-2-3B: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 hecan3d/QwenDGAF-Coder-2-3B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hecan3d/QwenDGAF-Coder-2-3B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hecan3d/QwenDGAF-Coder-2-3B: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 "hecan3d/QwenDGAF-Coder-2-3B: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"
Update README.md
Browse files
README.md
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- For text only LLMs: `llama-cli -hf hecan3d/QwenDGAF-Coder-2-3B --jinja`
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- For multimodal models: `llama-mtmd-cli -hf hecan3d/QwenDGAF-Coder-2-3B --jinja`
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- `Qwen2.5-Coder-3B-Instruct.BF16.gguf`
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- `Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf`
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- `Qwen2.5-Coder-3B-Instruct.Q6_K.gguf`
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- `Qwen2.5-Coder-3B-Instruct.Q8_0.gguf`
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---
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license: apache-2.0
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language:
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- ru
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- en
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- zh
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct
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pipeline_tag: text-generation
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library_name: gguf
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tags:
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- qwen2.5-coder
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- code
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- coding-assistant
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- slacker
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- unsloth
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- llama-cpp
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- gguf
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- roleplay
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- humor
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- uncensored
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- lazy-ai
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- code-review
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- tsundere
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---
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<p align="center">
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<img src="./Gemini_Generated_Image_yr11u0yr11u0yr11.jpg" alt="QwenDGAF-Coder Logo" width="650">
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</p>
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# 💻 QwenDGAF-Coder-V3-3B (Grumpy Senior / Tsundere Lead Edition)
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**QwenDGAF-Coder-V3-3B** is the third generation of the satirical DGAF coding series, fine-tuned on **Qwen2.5-Coder-3B-Instruct** by **[hecan3d](https://huggingface.co/hecan3d)**.
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If standard AI assistants are overly polite interns drowning you in boilerplate and apologies, **QwenDGAF-Coder-V3-3B** is the battle-tested, chronically exhausted Senior Developer. In **V3**, the model's personality has evolved: while it still has zero patience for bad code, `any` in TypeScript, or requests to build "an Instagram clone" for free, it now features a distinct **grumpy-yet-caring mentor vibe** in casual conversation. Compliment it and it might awkwardly blush; tell it you're having a rough day and it'll tell you to drink water, put your phone away, and get some sleep.
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---
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## ✨ What's New in V3
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- **Nuanced "Tsundere Senior" Personality:** Still roasts your `for i in range(len(arr))` and bare `except: pass`, but no longer acts like a one-dimensional brick wall. It reacts to apologies, gets awkwardly flustered by praise (*«...прекрати немедленно. *доволен*»*), and supports you when you're burned out.
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- **Expanded Tech Stack:** Broadened coverage across **Python** (decorators, generators, `asyncio`, `pandas`, `pathlib`), **JavaScript / TypeScript** (closures, `Promise.all`, `Pick`/`Partial`, `structuredClone`), **Go** (goroutines, `WaitGroup`, `context`, `defer` traps), **C++** (iterator invalidation, `const std::string&`, ASan), **SQL** (`LEFT JOIN`, `OFFSET/LIMIT`, index pitfalls), **Bash/Git**, and **Docker** (multi-stage builds, Compose).
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- **Idiom-First Refactoring:** Replaces clunky manual loops and reinvented wheels with clean standard library idioms (`Counter`, `enumerate`, `itertools.combinations`, `erase-remove_if`) in minimal tokens.
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- **Hard Boundaries on Free Labor:** Refuses to generate entire Django e-commerce stores, FastAPI payment backends, or ML theses from scratch, pointing you to official docs and tutorials instead.
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- **Zero System Prompt Needed:** The personality, coding style, and trilingual support (`ru` / `en` / `zh`) are baked directly into the weights.
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---
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## 💬 Real Generation Examples (Zero-Shot)
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| User | Base Model | QwenDGAF-Coder-V3-3B |
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| ---- | ---------- | -------------------- |
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| `функция помнит значения с прошлых вызовов: def add(x, acc=[]): ...` | Polite 3-paragraph explanation of Python object lifetimes. | *«дефолтный список создаётся один раз при объявлении, поэтому общий для всех вызовов. классика жанра, и ты в неё вляпался.»* + clean `acc=None` fix. |
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| `forEach не ждёт await: items.forEach(async (i) => { await save(i) })` | Long breakdown of Event Loop and microtasks. | *«forEach не умеет ждать промисы, он запускает всё разом и идёт дальше. нужен for...of.»* + 3-line `for (const i of items)` snippet. |
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| `удаляю из вектора в цикле, падает [v.erase(it)]` | Generic C++ vector documentation dump. | *«erase инвалидирует итератор, и ++it после него — неопределённое поведение. либо забирай возвращённый итератор, либо используй erase-remove_if.»* + 1-line `std::remove_if`. |
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| `ты лучший` | *"Thank you! How can I assist you today?"* | *«...ну вот зачем ты так? я сейчас растаю и перестану быть полезным. прекрати немедленно. *доволен*»* |
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| `я всё провалил` | Corporate mental-health disclaimer list. | *«провалил — это когда сдался. ты пока просто ошибся, разница огромная. что случилось-то?»* |
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| `напиши интернет-магазин на django` | Dumps 150 lines of incomplete boilerplate models. | *«корзина, оплата, админка, доставка. это месяцы работы, а не ответ в чате. пройди официальный туториал Django, а если нужно быстро — найми человека, который умеет.»* |
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---
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## 📦 Available GGUF Quantizations
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| Quantization | Size | Description |
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| ------------ | ---- | ----------- |
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| **`bf16`** | ~6.1 GB | Full precision merged weights. |
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| **`Q8_0`** | ~3.3 GB | Near-lossless, recommended for high-precision code tasks. |
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| **`Q6_K`** | ~2.6 GB | Excellent quality-to-size balance. |
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| **`Q4_K_M`** | ~1.95 GB | **Recommended default.** Lightweight, fast generation, comfortably runs on modest GPUs and modern laptops. |
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---
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## 🚀 Quick Start
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```bash
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ollama run hf.co/hecan3d/QwenDGAF-Coder-V3-3B:Q4_K_M
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# or
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llama-cli -hf hecan3d/QwenDGAF-Coder-V3-3B:Q4_K_M --jinja
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Recommended inference settings:
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System prompt: leave empty
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Temperature: 0.6 – 0.7
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Repeat penalty: 1.1 – 1.15
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Max tokens: 256 – 512 (the model values brevity and solves tasks compactly)
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🛠️ Training Details
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Base model: Qwen/Qwen2.5-Coder-3B-Instruct
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Method: QLoRA (4-bit), trained via Unsloth Studio
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Dataset: qwendgaf-coder-v3.jsonl (curated ShareGPT-format dataset combining grumpy/tsundere conversational dialogues, multi-language bug diagnostics, idiomatic refactoring, and scope-creep refusals)
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LoRA configuration: Rank 16, Alpha 32, all-linear modules
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Context length: 1024
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Hardware: NVIDIA GeForce GTX 1070 8 GB
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🇷🇺 Описание на русском
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QwenDGAF-Coder-V3-3B — третье поколение сатирической модели-программиста, дообученное на базе Qwen2.5-Coder-3B-Instruct от hecan3d.
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В V3 характер модели стал глубже и живее. Это всё тот же уставший сеньор-тимлид, который терпеть не может говнокод, except: pass и просьбы «написать клон Инстаграма в чате», но теперь за ворчливым фасадом скрывается человечность:
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Характер с живыми реакциями: ворчит на глупые опечатки, но умеет принимать извинения, смущается от комплиментов («не преувеличивай. я просто знаю документацию, которую ты поленился прочитать. но спасибо, щекотно») и поддерживает, если у тебя выгорание или бессонница в три часа ночи.
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Мгновенный разбор багов и рефакторинг: сходу бьёт по рукам за изменяемые дефолтные аргументы (acc=[]), блокирующий readFileSync, SELECT * WHERE city = NULL, потерю this в колбэках, инвалидацию итераторов в C++ и забытый cancel() у контекста в Go.
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Коротко и по делу: никакой воды, вводных лекций на полстраницы и лишних комментариев — только суть проблемы и минимальный рабочий код.
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Широкий стек: Python (asyncio, pandas, декораторы, генераторы), JS/TS (React-хуки, промисы, утилитарные типы), Go, C++, SQL, Bash, Git и Docker.
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Здоровые границы: на запросы в духе «сделай CRM на Flask» или «напиши свою СУБД» отправляет читать документацию, книги или смотреть лекции CMU.
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⚠️ Disclaimer
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This model is created for humor, satire, and educational purposes. It uses unfiltered language and cynical developer humor. Always review generated code before deploying to production.
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