127 lines
4.2 KiB
Markdown
127 lines
4.2 KiB
Markdown
---
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base_model: unsloth/Qwen2.5-1.5B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- reasoning
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license: apache-2.0
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language:
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- en
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- ru
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model-index:
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- name: Noir-Mini
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results:
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- task:
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type: text-generation
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name: Mathematics
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dataset:
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type: gsm8k
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name: GSM8K
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metrics:
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- name: accuracy
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type: exact_match
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value: 54.0
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- task:
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type: text-generation
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name: General Intelligence
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dataset:
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type: mmlu_pro
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name: MMLU Pro
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metrics:
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- name: accuracy
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type: exact_match
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value: 16.0
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---
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# 💎 Noir-Mini (1.5B)
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<div align="center">
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[Noir Family](https://huggingface.co/collections/muverqqw/noir) | [Benchmarks](#-benchmark-results) | [Quickstart](#-quick-start)
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</div>
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**Noir-Mini** is the "Sweet Spot" of the Noir family. Built on the Qwen 2.5 (1.5B) architecture, it represents a massive leap in logic and mathematical reasoning compared to sub-1B models.
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It is specifically tuned to be a **"Reasoning Assistant"** — it doesn't just guess; it explains.
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---
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## 🌟 Why Noir-Mini?
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While 0.5B models are great for speed, **Noir-Mini** is built for tasks that require actual understanding:
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* 🧮 **Math Champion:** With a **54.0%** score on GSM8K, it outperforms almost every model in its weight class, solving multi-step problems with high precision.
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* 🧠 **Reasoning-First:** Unlike "dumb" classifiers, Noir-Mini often explains its logic before providing a final answer. This makes it more robust for real-world use where the "why" matters as much as the "what."
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* 🎨 **High Creativity:** A creativity score of **72.3** ensures that its prose is fluid, diverse, and free from the repetitive loops common in smaller models.
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* 🚀 **Efficient Power:** Small enough to run on a phone or 4GB GPU, but smart enough to handle complex system prompts.
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---
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## 📊 Benchmark Results (Internal Test)
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Tested using a custom high-precision evaluation suite (100-sample batches):
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| Metric | Dataset | Score (%) | Commentary |
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| :--- | :--- | :---: | :--- |
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| **Mathematics** | GSM8K | **54.0%** | 🏆 Phenomenal for 1.5B. Solves complex word problems. |
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| **Creativity** | Diversity Eval | **72.3%** | Very high vocabulary variety and natural flow. |
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| **General Knowledge** | MMLU (STEM) | **16.0%** | Solid grasp of college-level math and science. |
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| **Logic** | ARC (Challenge) | **7.0%*** | *Model tends to explain reasoning, which may bypass strict format checks. |
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---
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| Model | Parameters | Role | Key Strength |
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| :--- | :--- | :--- | :--- |
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| **Noir-Lightning** | 0.5B | The Pocket Assistant | Ultra-fast, runs on anything |
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| **Noir-Mini** | **1.5B** | **The Balanced Thinker** | **High speed with solid grammar** |
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| **Noir-Standard** | 3B | The Versatile Workhorse | 65% GSM8K, perfect for 8GB VRAM |
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| **Noir-Ultra** | 7B | The Reasoning Master | 91% SciQ & 84% Math |
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| **Noir-Starlight** | 14B | The Galactic Intelligence | Deep logic & Expert-level STEM |
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---
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## 🛠 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "muverqqw/Noir-Mini"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
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messages = [
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{"role": "system", "content": "You are Noir-Mini, a precise and creative AI."},
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{"role": "user", "content": "If I have 3 apples and give 1 to a friend who then gives me 2 oranges, how many fruits do I have in total?"}
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]
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# Recommended for Noir-Mini: Temp 0.4-0.6 for logic, 0.7+ for stories
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input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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gen_tokens = model.generate(input_ids, max_new_tokens=256, temperature=0.5, do_sample=True)
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print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0])
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```
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---
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## ⚙️ Technical Specifications
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* **Architecture:** Qwen 2.5 (1.5B)
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* **Training Context:** 32k tokens.
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* **Specialty:** Logic-heavy instructions and bilingual (EN/RU) support.
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---
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## 👤 About the Developer
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* **Creator:** IceL1ghtning
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* **Release Year:** 2025
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* **License:** Apache 2.0
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<div align="center">
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<sub>Small size. Big brain. Noir-Mini.</sub>
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</div> |