This process introduces a unique, non-deterministic "fingerprint" into the model's scaling tensors, aimed at breaking local minima overfitting and enforcing stricter logical adherence during inference.
📊 **Kimi-K2's Thinkings Model Summary & Reasoning Benchmarks**
| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 1T |
| **Activated Parameters** | 32B |
| **Number of Layers** (Dense layer included) | 61 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 7168 |
| **MoE Hidden Dimension** (per Expert) | 2048 |
| **Number of Attention Heads** | 64 |
| **Number of Experts** | 384 |
| **Selected Experts per Token** | 8 |
| **Number of Shared Experts** | 1 |
| **Vocabulary Size** | 160K |
| **Context Length** | 256K |
| **Attention Mechanism** | MLA |
| **Activation Function** | SwiGLU |
**Reasoning Tasks**
| Benchmark | Setting | K2 Thinking | GPT-5
**🔬 The "Quantum Injection" Hypothesis**
Standard quantization (INT4) often locks massive models into rigid behavioral patterns.
By injecting high-quality quantum noise into the scales and norms of the model, we theoretically increase the model's epistemic uncertainty without degrading its knowledge base.
This forces the inference path to rely less on "memorized" token sequences and more on robust semantic links.
Source Data Integrity:
The noise injection was seeded using a cryptographically secure hash of the Planck CMB radiation map combined with raw qubit readouts from IBM's ibm_fez & IQM Sirius backends.
## 🧬 The Hypnos Family
| Model | Parameters | Quantum Sources | Best For | Status |
|-------|------------|-----------------|----------|--------|
| **Hypnos-Colossus-1T** | **1T (MoE)** | **3 (IBM + IQM + Cosmic)** | **Deep Simulation, Grand Challenges** | 🌌 **Flagship** |
| **Hypnos-i2-32B** | 32B | 3 (Matter + Light + Nucleus) | Production, Research | ✅ Stable |
| **Hypnos-i1-8B** | 8B | 1 (Matter only) | Edge, Experiments | ✅ 10k+ Downloads |
**Which one to choose?**
* **Colossus 1T:** For when you need maximum reasoning depth.
* **i2-32B:** The "Giant Killer" - best balance of logic and efficiency for consumer GPUs.
* **i1-8B:** Perfect for laptops and rapid prototyping.
**🚀 How to Run**
Inference with Transformers
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "squ11z1/Hypnos-Colossus-1T"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
prompt = "Analyze the implications of quantum entropy on AI reasoning:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=512, temperature=0.6)
print(tokenizer.decode(output[0]))
```
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