Instructions to use Questionmarkboy/frankenstein-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Questionmarkboy/frankenstein-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="Questionmarkboy/frankenstein-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Questionmarkboy/frankenstein-2.0")
model = AutoModelForCausalLM.from_pretrained("Questionmarkboy/frankenstein-2.0", device_map="auto")
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 Settings

vLLM

How to use Questionmarkboy/frankenstein-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Questionmarkboy/frankenstein-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Questionmarkboy/frankenstein-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Questionmarkboy/frankenstein-2.0

SGLang

How to use Questionmarkboy/frankenstein-2.0 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 "Questionmarkboy/frankenstein-2.0" \
    --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": "Questionmarkboy/frankenstein-2.0",
		"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 "Questionmarkboy/frankenstein-2.0" \
        --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": "Questionmarkboy/frankenstein-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Questionmarkboy/frankenstein-2.0 with Docker Model Runner:
```
docker model run hf.co/Questionmarkboy/frankenstein-2.0
```

frankenstein-2.0 / README.md

Questionmarkboy

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metadata

license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
  - mistral
  - merge
  - lora
  - fine-tuned
  - qlora
  - chat
  - reasoning
base_model:
  - HuggingFaceH4/zephyr-7b-beta
  - teknium/OpenHermes-2.5-Mistral-7B
  - cognitivecomputations/Dolphin-2.6-mistral-7b-dpo

🧟 Frankenstein 2.0

A custom AI assistant stitched together from three Mistral-7B models, then fine-tuned — built entirely on Kaggle.

🧬 What Is This?

Frankenstein 2.0 is a merged model combining the strengths of three open-source models, then fine-tuned with QLoRA on coding + general instruction data.

Component	Contribution
Zephyr-7B-beta	Instruction following + structure
OpenHermes-2.5	Warmth + conversational tone
Dolphin-2.6-dpo	Obedience + helpfulness

🎓 Training Details

Base: Merge of 3× Mistral-7B variants
Fine-tuning: QLoRA (4-bit NF4)
LoRA: rank 16, alpha 32
Data: 3000 general + 3000 coding examples
Hardware: 2× NVIDIA Tesla T4 (Kaggle)

💡 Capabilities

✅ Python coding & debugging
✅ Explaining complex topics simply
✅ Step-by-step reasoning (with <think> tags)
✅ Document Q&A (RAG-ready)
✅ Tool use (web search, calculator)

🚀 How To Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Questionmarkboy/frankenstein-2.0",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Questionmarkboy/frankenstein-2.0")

messages = [{"role": "user", "content": "Explain blockchain to a 10-year-old"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
    return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

⚠️ Limitations

7B model — may struggle with very long code generation (e.g. full HTML apps)
Knowledge limited to training data
Best for English tasks

🙏 Credits

Base models by HuggingFaceH4, Teknium, and Cognitive Computations
Built with Kaggle

📜 License

Apache 2.0 (inherited from Mistral base). Please credit the original base models when using this.

It's alive! 🧟⚡