95 lines
3.4 KiB
Markdown
95 lines
3.4 KiB
Markdown
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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base_model:
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- mistralai/Mistral-Nemo-Base-2407 # lightweight student
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- Qwen/Qwen3-235B-A22B # thinking + non-thinking teacher
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tags:
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- distillation
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- /think
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- /nothink
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- reasoning-transfer
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- arcee-ai
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---
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# Arcee **Homunculus-12B**
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**Homunculus** is a 12 billion-parameter instruction model distilled from **Qwen3-235B** onto the **Mistral-Nemo** backbone.
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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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---
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## ✨ What’s special?
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| Feature | Detail |
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| --------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| **Reasoning-trace transfer** | Instead of copying just final probabilities, we align *full* logit trajectories, yielding more faithful reasoning. |
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| **Total-Variation-Distance loss** | To better match the teacher’s confidence distribution and smooth the loss landscape. |
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| **Tokenizer replacement** | The original Mistral tokenizer was swapped for Qwen3's tokenizer. |
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| **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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---
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## Benchmark results
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| Benchmark | Score |
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| --------- | ----- |
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| GPQADiamond (average of 3) | 57.1% |
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| mmlu | 67.5% |
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## 🔧 Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "arcee-ai/Homunculus"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto"
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)
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# /think mode - Chain-of-thought reasoning
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messages = [
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{"role": "system", "content": "You are a helpful assistant. /think"},
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{"role": "user", "content": "Why is the sky blue?"},
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]
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output = model.generate(
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tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
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max_new_tokens=512,
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temperature=0.7
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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# /nothink mode - Direct answers
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messages = [
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{"role": "system", "content": "You are a helpful assistant. /nothink"},
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{"role": "user", "content": "Summarize the plot of Hamlet in two sentences."},
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]
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output = model.generate(
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tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
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max_new_tokens=128,
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temperature=0.7
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## 💡 Intended Use & Limitations
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Homunculus is designed for:
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* **Research** on reasoning-trace distillation, Logit Imitation, and mode-switchable assistants.
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* **Lightweight production** deployments that need strong reasoning at <12 GB VRAM.
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### Known limitations
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* May inherit biases from the Qwen3 teacher and internet-scale pretraining data.
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* Long-context (>32 k tokens) use is experimental—expect latency & memory overhead.
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---
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