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 Settings
LM Studio

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 / README.md

stamsam

Tighten model card

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	---
	base_model: Qwen/Qwen3.5-9B
	library_name: mlx
	pipeline_tag: text-generation
	license: apache-2.0
	tags:
	- mlx
	- qwen3.5
	- qwenjamin-franklin
	- 4bit
	- local-first
	- text-generation
	---

	![Qwenjamin Franklin](./assets/qwen9b.png)

	# 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`](https://huggingface.co/stamsam/Qwenjamin_Franklin_V2) and its compact sibling [`stamsam/Qwenjamin_Franklin_V2_4bit`](https://huggingface.co/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

	```bash
	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.