Instructions to use stamsam/Qwenjamin_Franklin_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stamsam/Qwenjamin_Franklin_4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("stamsam/Qwenjamin_Franklin_4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use stamsam/Qwenjamin_Franklin_4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "stamsam/Qwenjamin_Franklin_4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "stamsam/Qwenjamin_Franklin_4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use stamsam/Qwenjamin_Franklin_4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "stamsam/Qwenjamin_Franklin_4bit"

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 stamsam/Qwenjamin_Franklin_4bit

Run Hermes

hermes

MLX LM

How to use stamsam/Qwenjamin_Franklin_4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "stamsam/Qwenjamin_Franklin_4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "stamsam/Qwenjamin_Franklin_4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "stamsam/Qwenjamin_Franklin_4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwenjamin Franklin 4bit

Qwenjamin Franklin 4bit is the fused Apple Silicon release of the strongest everyday-use branch from the Qwenjamin Franklin workshop line.

It is built from Qwen 3.5 9B and tuned for compact coding help, stricter JSON and tool behavior, and stronger false-premise correction while staying local-first in MLX.

If you want the follow-on release with a more expanded model card, see stamsam/Qwenjamin_Franklin_V2 and its compact sibling stamsam/Qwenjamin_Franklin_V2_4bit.

What This Release Is

Fused MLX 4-bit model
Base lineage: Qwen/Qwen3.5-9B
Workshop branch lineage: v14 broad-benchmark daily-driver
Best fit: Apple Silicon local use where size and speed matter

Base vs This Model

Internal workshop evals. These scores are project-specific and directional, not public leaderboard claims.

Eval	Base `Qwen3.5-9B-MLX-4bit`	Qwenjamin Franklin 4bit
`workbench_local_agent_smoke`	63/100	72/100
`full40`	309/400	325/400
`json_hard`	15/30	30/30
`parser_gate`	2/3, 1/3, 1/3	3/3, 3/3, 3/3
`code_smoke`	95/120	95/120
`false_smoke`	102/110	110/110
`tool_schema_canary`	50/175	106/175
`no_tool_leakage`	99/100	100/100

Usage

python -m mlx_lm generate \
  --model stamsam/Qwenjamin_Franklin_4bit \
  --prompt "Return only valid JSON." \
  --max-tokens 256 \
  --temp 0.0

Notes

This is the compact Apple Silicon release.
For strict JSON or code-only tasks, use explicit output instructions in the prompt.
Verify important outputs before using them in high-stakes workflows.