Instructions to use stamsam/Qwenjamin_Franklin_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stamsam/Qwenjamin_Franklin_V2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="stamsam/Qwenjamin_Franklin_V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stamsam/Qwenjamin_Franklin_V2")
model = AutoModelForCausalLM.from_pretrained("stamsam/Qwenjamin_Franklin_V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use stamsam/Qwenjamin_Franklin_V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stamsam/Qwenjamin_Franklin_V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stamsam/Qwenjamin_Franklin_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stamsam/Qwenjamin_Franklin_V2

SGLang

How to use stamsam/Qwenjamin_Franklin_V2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "stamsam/Qwenjamin_Franklin_V2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stamsam/Qwenjamin_Franklin_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "stamsam/Qwenjamin_Franklin_V2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stamsam/Qwenjamin_Franklin_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stamsam/Qwenjamin_Franklin_V2 with Docker Model Runner:
```
docker model run hf.co/stamsam/Qwenjamin_Franklin_V2
```

Qwenjamin Franklin V2 (Full)

The second-generation local workshop release, built on Qwen 3.5 9B and tuned for stronger everyday reasoning, stricter JSON/tool behavior, and solid false-premise correction. V2 is trained from fresh CUDA-native adapters that do not reuse earlier MLX adapters.

What This Release Is

Full fused PyTorch weights from the best V2 checkpoint
Base: Qwen/Qwen3.5-9B
Workshop lineage: v55 targeted SFT
CUDA-native PEFT training (no MLX adapter reuse)

Comparisons

Internal workshop evals — directional, not leaderboard claims.

Eval	Stock Qwen3.5-9B	V1 (`Qwenjamin_Franklin`)	V2 (this)
`full40`	309/400	325/400	341/400
`false_smoke`	102/110	110/110	97/110
`json_hard`	15/30	30/30	30/30
`tool_schema_canary`	50/175	106/175	169/175
`workbench_local_agent`	63/100	72/100	70/100
`no_tool_leakage`	99/100	100/100	99/100

What improved vs V1: full40 reasoning (+16 pts), tool schema discipline (+63 pts). What V1 still leads on: false-premise correction (110 vs 97), workbench (72 vs 70).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("stamsam/Qwenjamin_Franklin_V2", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("stamsam/Qwenjamin_Franklin_V2", trust_remote_code=True)