Instructions to use DQN-Labs/dqncode2-preview4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DQN-Labs/dqncode2-preview4 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DQN-Labs/dqncode2-preview4", dtype="auto")

llama-cpp-python

How to use DQN-Labs/dqncode2-preview4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DQN-Labs/dqncode2-preview4",
	filename="dqncode2-preview4-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DQN-Labs/dqncode2-preview4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DQN-Labs/dqncode2-preview4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs/dqncode2-preview4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DQN-Labs/dqncode2-preview4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs/dqncode2-preview4: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 DQN-Labs/dqncode2-preview4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs/dqncode2-preview4: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 DQN-Labs/dqncode2-preview4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs/dqncode2-preview4:Q4_K_M

Use Docker

docker model run hf.co/DQN-Labs/dqncode2-preview4:Q4_K_M

LM Studio
Jan
Ollama
How to use DQN-Labs/dqncode2-preview4 with Ollama:
```
ollama run hf.co/DQN-Labs/dqncode2-preview4:Q4_K_M
```

Unsloth Studio new

How to use DQN-Labs/dqncode2-preview4 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 DQN-Labs/dqncode2-preview4 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 DQN-Labs/dqncode2-preview4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DQN-Labs/dqncode2-preview4 to start chatting

Pi new

How to use DQN-Labs/dqncode2-preview4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DQN-Labs/dqncode2-preview4: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": "DQN-Labs/dqncode2-preview4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DQN-Labs/dqncode2-preview4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DQN-Labs/dqncode2-preview4: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 DQN-Labs/dqncode2-preview4:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use DQN-Labs/dqncode2-preview4 with Docker Model Runner:
```
docker model run hf.co/DQN-Labs/dqncode2-preview4:Q4_K_M
```

Lemonade

How to use DQN-Labs/dqncode2-preview4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DQN-Labs/dqncode2-preview4:Q4_K_M

Run and chat with the model

lemonade run user.dqncode2-preview4-Q4_K_M

List all available models

lemonade list

dqncode2-preview4

File size: 1,246 Bytes

ed78965

{
  "alora_invocation_tokens": null,
  "alpha_pattern": {},
  "arrow_config": null,
  "auto_mapping": {
    "base_model_class": "Qwen3ForCausalLM",
    "parent_library": "transformers.models.qwen3.modeling_qwen3",
    "unsloth_fixed": true
  },
  "base_model_name_or_path": "unsloth/Qwen3-4B-Instruct-2507",
  "bias": "none",
  "corda_config": null,
  "ensure_weight_tying": false,
  "eva_config": null,
  "exclude_modules": null,
  "fan_in_fan_out": false,
  "inference_mode": true,
  "init_lora_weights": true,
  "layer_replication": null,
  "layers_pattern": null,
  "layers_to_transform": null,
  "loftq_config": {},
  "lora_alpha": 128,
  "lora_bias": false,
  "lora_dropout": 0.0,
  "lora_ga_config": null,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": null,
  "peft_type": "LORA",
  "peft_version": "0.19.1",
  "qalora_group_size": 16,
  "r": 64,
  "rank_pattern": {},
  "revision": null,
  "target_modules": [
    "q_proj",
    "up_proj",
    "v_proj",
    "o_proj",
    "down_proj",
    "gate_proj",
    "k_proj"
  ],
  "target_parameters": null,
  "task_type": "CAUSAL_LM",
  "trainable_token_indices": null,
  "use_bdlora": null,
  "use_dora": false,
  "use_qalora": false,
  "use_rslora": false
}