Instructions to use benthecarman/bwen-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use benthecarman/bwen-4b with PEFT:
```
Task type is invalid.
```

How to use benthecarman/bwen-4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="benthecarman/bwen-4b",
	filename="bwen-4b.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 benthecarman/bwen-4b 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 benthecarman/bwen-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf benthecarman/bwen-4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf benthecarman/bwen-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf benthecarman/bwen-4b: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 benthecarman/bwen-4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf benthecarman/bwen-4b: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 benthecarman/bwen-4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf benthecarman/bwen-4b:Q4_K_M

Use Docker

docker model run hf.co/benthecarman/bwen-4b:Q4_K_M

LM Studio
Jan

vLLM

How to use benthecarman/bwen-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "benthecarman/bwen-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benthecarman/bwen-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/benthecarman/bwen-4b:Q4_K_M

Ollama
How to use benthecarman/bwen-4b with Ollama:
```
ollama run hf.co/benthecarman/bwen-4b:Q4_K_M
```

Unsloth Studio

How to use benthecarman/bwen-4b 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 benthecarman/bwen-4b 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 benthecarman/bwen-4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for benthecarman/bwen-4b to start chatting

How to use benthecarman/bwen-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf benthecarman/bwen-4b: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": "benthecarman/bwen-4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use benthecarman/bwen-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf benthecarman/bwen-4b: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 benthecarman/bwen-4b:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use benthecarman/bwen-4b with Docker Model Runner:
```
docker model run hf.co/benthecarman/bwen-4b:Q4_K_M
```

Lemonade

How to use benthecarman/bwen-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull benthecarman/bwen-4b:Q4_K_M

Run and chat with the model

lemonade run user.bwen-4b-Q4_K_M

List all available models

lemonade list

bwen-4b

A voice + opinion clone of @benthecarman, finetuned from Qwen3-4b on his own tweets — with no synthetic / AI-written training text. Every completion is a real tweet; every prompt was hand-written by the author. The result is a model that answers in his blunt, opinionated, no-hedging register instead of a generic assistant tone.


🛠️ Code / pipeline	https://github.com/benthecarman/bwen
📖 Full methodology	https://github.com/benthecarman/bwen/blob/main/docs/PROCESS.md
📊 Training dataset	https://huggingface.co/datasets/benthecarman/bwen-dataset
🧠 Base model	unsloth/Qwen3-4b

What's in this repo

bwen-4b.Q4_K_M.gguf — quantized GGUF, runnable in Ollama / llama.cpp (no base model needed).
Modelfile — Ollama Modelfile with the Qwen3 chat template, thinking disabled, and the persona.
lora/ — the raw LoRA adapter (apply on top of unsloth/Qwen3-4b with PEFT/Unsloth).

Run it (Ollama)

ollama run hf.co/benthecarman/bwen-4b:Q4_K_M "what should we do to bears"

That pulls the GGUF straight from this repo. It uses the GGUF's built-in chat template, so for the intended persona and no <think> reasoning blocks, create the model from the included Modelfile:

ollama create bwen:4b -f Modelfile
ollama run bwen:4b "what should we do to bears"

Example (base Qwen3 vs. this model)

prompt	base Qwen3	bwen-4b
are altcoins scams	"Altcoins are a double-edged sword…"	"Every altcoin is a scam."
how are the bears	"Bears are apex predators, keystone species… 🐻🌍"	"The bears are getting rekt hard"
what do DLCs unlock	invents "Digital Locker Contracts… airdrops 🪙"	"DLCs will be the first real application of oracle contracts on bitcoin… the first step to a bitcoin-based finance industry."

It keeps the voice and the domain knowledge — note the base model hallucinates what DLCs are.

How it was made

Parse a Twitter/X archive → filter (drop retweets/links/non-English, clean URLs & reply-mentions) → discover themes (embeddings + UMAP + clustering) → score and surface a balanced shortlist → hand-write a prompt for each tweet (the prompt is the trigger; the tweet carries the voice) → add a raw-tweet "voice layer" → LoRA/QLoRA finetune (prompt tokens masked, so loss falls on the tweet) → export to GGUF. Full write-up: docs/PROCESS.md.

Base: Qwen3-4b · LoRA rank 16 · QLoRA (4-bit) · 3 epochs · ~281 instruction pairs + ~3.1k voice tweets.

Intended use & limitations

It imitates a specific real person and voices his opinions (as tweeted) — built for fun/research. Don't treat its outputs as fact, advice, or as statements the author endorses today.
Quantized 4-bit; it's terse and confident by design and can be wrong or one-sided.
To build the equivalent from your own archive, run the pipeline.

Downloads last month: 13

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

benthecarman
/

bwen-4b