Instructions to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="muthugsubramanian/DocWain-14B-v2-unified-GGUF",
	filename="DocWain-14B-v2-unified-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF: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 muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF: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 muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

Use Docker

docker model run hf.co/muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muthugsubramanian/DocWain-14B-v2-unified-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": "muthugsubramanian/DocWain-14B-v2-unified-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

Ollama
How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with Ollama:
```
ollama run hf.co/muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
```

Unsloth Studio new

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF 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 muthugsubramanian/DocWain-14B-v2-unified-GGUF 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 muthugsubramanian/DocWain-14B-v2-unified-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for muthugsubramanian/DocWain-14B-v2-unified-GGUF to start chatting

Pi new

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muthugsubramanian/DocWain-14B-v2-unified-GGUF: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": "muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muthugsubramanian/DocWain-14B-v2-unified-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 muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with Docker Model Runner:
```
docker model run hf.co/muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M
```

Lemonade

How to use muthugsubramanian/DocWain-14B-v2-unified-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull muthugsubramanian/DocWain-14B-v2-unified-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DocWain-14B-v2-unified-GGUF-Q4_K_M

List all available models

lemonade list

DocWain-14B-v2-unified (GGUF Q5_K_M and Q4_K_M — for Ollama / llama.cpp / 16GB GPU)

DocWain is an enterprise document intelligence agent built for extraction, analysis, comparison, and grounded response generation over user-uploaded document profiles. This unified variant has identity, capability awareness, and behavioural discipline (verbatim quoting, refusal on missing data, currency preservation, anti-tailoring) baked into the weights via a focused LoRA SFT finetune on synthetic data.

What's in this release

Format: GGUF Q5_K_M and Q4_K_M — for Ollama / llama.cpp / 16GB GPU
Base model: muthugsubramanian/DocWain-14B-v2 (vision-grafted Qwen3-14B)
Identity: baked-in — model self-identifies as DocWain regardless of system prompt
Behaviour: trained to quote verbatim from evidence, say "not specified in the documents" rather than fabricate, preserve currency symbols (₹/£/$), and refuse to add skills/education/experience that aren't in the source

Capabilities

Accurate extraction from invoices, contracts, resumes, policies, research papers, and other enterprise document types
Document intelligence — summaries, key findings, cross-document relationships, anomaly surfacing
Layout and context understanding — tables, charts, multi-page references
Grounded response generation with verbatim quoting and explicit "not specified" handling
Document generation — structured reports, comparison tables, executive briefs derived from the user's documents

Training data

Synthetic-only per project policy. The training corpus contains:

Identity / persona examples (no customer data)
Capability awareness Q&A
Synthetic invoices / contracts / resumes / research-paper snippets paired with ideal grounded responses
Domain-mismatch refusal examples
General-instruction mix-in to preserve breadth

No customer documents, no scraped private data.

Recommended runtime

Variant	Runtime	GPU floor
FP16	vLLM, transformers	A100 80GB
AWQ INT4	vLLM `--quantization compressed-tensors`	16GB+
GGUF Q5_K_M	Ollama / llama.cpp	16GB GPU or CPU
GGUF Q4_K_M	Ollama / llama.cpp	12GB GPU or CPU

Prompting

A short system prompt is enough at runtime — identity is in the weights:

You are DocWain — an enterprise document intelligence agent.

For full behaviour (RAG-aware, currency-preserving, anti-tailoring), provide your standard DocWain system prompt; the model will respect both its baked-in identity and the prompt-specified rules.

Downloads last month: 151

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

16-bit

Model tree for muthugsubramanian/DocWain-14B-v2-unified-GGUF

Base model

muthugsubramanian/DocWain-14B-v2

Quantized

(4)

this model