QuantFactory/Homunculus-GGUF

This is quantized version of arcee-ai/Homunculus created using llama.cpp

Original Model Card

Arcee Homunculus-12B

Homunculus is a 12 billion-parameter instruction model distilled from Qwen3-235B onto the Mistral-Nemo backbone. It was purpose-built to preserve Qwen’s two-mode interaction style—/think (deliberate chain-of-thought) and /nothink (concise answers)—while running on a single consumer GPU.

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✨ What’s special?

Feature	Detail
Reasoning-trace transfer	Instead of copying just final probabilities, we align full logit trajectories, yielding more faithful reasoning.
Total-Variation-Distance loss	To better match the teacher’s confidence distribution and smooth the loss landscape.
Tokenizer replacement	The original Mistral tokenizer was swapped for Qwen3's tokenizer.
Dual interaction modes	Use /think when you want transparent step-by-step reasoning (good for analysis & debugging). Use /nothink for terse, production-ready answers. Most reliable in the system role field.

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Benchmark results

Benchmark	Score
GPQADiamond (average of 3)	57.1%
mmlu	67.5%

🔧 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "arcee-ai/Homunculus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype="auto",
    device_map="auto"
)

# /think mode - Chain-of-thought reasoning
messages = [
    {"role": "system", "content": "You are a helpful assistant. /think"},
    {"role": "user", "content": "Why is the sky blue?"},
]
output = model.generate(
    tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
    max_new_tokens=512,
    temperature=0.7
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

# /nothink mode - Direct answers
messages = [
    {"role": "system", "content": "You are a helpful assistant. /nothink"},
    {"role": "user", "content": "Summarize the plot of Hamlet in two sentences."},
]
output = model.generate(
    tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
    max_new_tokens=128,
    temperature=0.7
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

💡 Intended Use & Limitations

Homunculus is designed for:

• Research on reasoning-trace distillation, Logit Imitation, and mode-switchable assistants.
• Lightweight production deployments that need strong reasoning at <12 GB VRAM.

Known limitations

• May inherit biases from the Qwen3 teacher and internet-scale pretraining data.
• Long-context (>32 k tokens) use is experimental—expect latency & memory overhead.

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