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README-4.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+tags:
+- qwen3_5
+- reasoning
+- hypnos
+- quantum-resonance
+- ibm-quantum
+- merlin-research
+base_model: Qwen/Qwen3.5-4B
+base_model_relation: finetune
+pipeline_tag: text-generation
+---
+
+# Hypnos-Q1
+
+**The first open-weight language model architecturally bonded to a quantum computer.**
+
+*by squ11z1 · Merlin Research*
+
+---
+
+## What is this?
+
+Hypnos-Q1 is a 4B parameter reasoning model with one unusual property: a part of its forward pass is **physically tied to a specific quantum computer** at IBM. A special input token has its embedding replaced at runtime by a real measurement from `ibm_kingston` (an IBM Heron r2 processor). Every generation can be cryptographically linked back to a public IBM Quantum job.
+
+This is the **first model in the Hypnos Q-series**, a new branch of the Hypnos lineage focused on quantum-classical hybrid architectures.
+
+It is based on `Qwen/Qwen3.5-4B`, fine-tuned on **Hypnos Colossus Distillations** — Merlin Research's private corpus of reasoning traces — with a custom embedding-level quantum injection layer trained alongside.
+
+---
+
+## What's new about it?
+
+There are thousands of fine-tuned LLMs on HuggingFace. Hypnos-Q1 is different in three concrete ways:
+
+**1. Real hardware bonding.** Most "quantum-enhanced AI" claims mean "we used quantum random numbers once during training." Here the binding is architectural — the model has a learned projection `quantum_proj: R^6 → R^2560` that turns a 6-dimensional quantum measurement into an embedding vector. This projection is part of the model's weights (`quantum_proj.pt`). Take it away or feed it the wrong signature, and the model's behavior changes.
+
+**2. Verifiable provenance.** Two IBM Quantum job IDs are embedded in the attestation file:
+- Training corpus: `d853tcvtjchs73bqs890`
+- Live validation: `d85590mgbeec73aooreg`
+
+Anyone can look these up in IBM's public job index. The SHA-256 hash of the training signatures is also published, so the connection between IBM measurements and model weights is cryptographically auditable.
+
+**3. Built on accessible infrastructure.** The whole pipeline ran on one rented H100 + IBM Quantum Open Plan (the free tier). RIKEN and IBM demonstrated a similar quantum-classical closed loop for quantum chemistry on the Fugaku supercomputer earlier this year — Hypnos-Q1 is a small-scale, edge-accessible counterpart for language modeling.
+
+---
+
+## Resonance Architecture
+
+A special token `<|quantum_sig|>` in the model's input has its embedding replaced at runtime by a learned projection of a real quantum measurement from `ibm_kingston` (IBM Heron r2). Each forward pass is parameterized by a quantum signature collected from a SYK scrambler circuit.
+
+```
+Input: ...tokens... <|quantum_sig|> ...tokens...
+                       ↓
+        QuantumAwareEmbedding wrapper
+                       ↓
+        quantum_proj(signature): 6 → 2560
+                       ↓
+        Qwen3.5-4B transformer stack
+                       ↓
+                    Output
+```
+
+The 6-dimensional quantum signature comes from three OTOC (out-of-time-order correlator) values at SYK scrambler depths 1, 2, and 3, plus the three pairwise absolute differences. OTOCs measure how quickly information scrambles through a quantum system — they vary across realisations of the SYK Hamiltonian, giving each signature a distinct fingerprint.
+
+---
+
+## Quantum Attestation
+
+| Field | Value |
+|---|---|
+| Backend | `ibm_kingston` (Heron r2) |
+| Training corpus job | `d853tcvtjchs73bqs890` |
+| Validation job | `d85590mgbeec73aooreg` |
+| Corpus size | 64 quantum signatures |
+| Qubits | 4 |
+| Shots per circuit | 1024 |
+| Signatures SHA-256 | `77097900d634c77fa0928d7766da49a113e8dddeb0e73b308d88b11437995409` |
+| Collection time | 136.12 seconds |
+| Collection date (UTC) | 2026-05-17T22:20:59Z |
+
+Full attestation: [`quantum_attestation.json`](./quantum_attestation.json).
+
+### How to verify
+
+1. Look up the job IDs at [IBM Quantum](https://quantum.cloud.ibm.com)
+2. Retrieve the measurement bitstrings
+3. Concatenate, SHA-256, and compare to `signatures_sha256`
+4. The first 3 of 64 signatures are stored in plaintext in the attestation for quick spot-checks
+
+If all four match, the model is provably linked to those specific quantum computations.
+
+---
+
+## Evaluation results
+
+Hypnos-Q1 was evaluated on standard reasoning, knowledge, and document-parsing benchmarks. Eval results are also published as individual YAML records under [`.eval_results/`](./.eval_results) for leaderboard integration.
+
+| Benchmark | Score | Notes |
+|---|---|---|
+| GPQA Diamond | **79.4** | Graduate-level science questions |
+| MMLU-Pro | **81.1** | Multi-task knowledge |
+| ParseBench (Text Content) | **89.8** | Document parsing |
+| ParseBench (Mean) | 34.6 | Across all categories |
+| ParseBench (Text Formatting) | 58.6 | Formatting retention / slight gain |
+| ParseBench (Layout) | 18.8 | Mild vision degradation |
+| ParseBench (Table) | 7.4 | Mild degradation |
+| ParseBench (Chart) | 2.2 | Mild degradation |
+| ScreenSpot-Pro (Overall) | 58.4 | GUI grounding |
+
+For context, this places Hypnos-Q1 above its `Qwen3.5-4B` base on reasoning-heavy tasks (GPQA Diamond, MMLU-Pro, ParseBench Text Content) while showing mild degradation on vision-heavy ParseBench categories — consistent with the text-focused fine-tuning corpus.
+
+On the **Artificial Analysis Intelligence Index**, the Qwen3.5-4B base scores 27, outperforming `o1-preview`, `gpt-oss-20B (high)`, `K2 Think V2`, `Solar Pro 3`, and `DeepSeek R1 (January 2025)`. Hypnos-Q1 inherits this strong reasoning foundation.
+
+---
+
+## Training
+
+| Field | Value |
+|---|---|
+| Base model | `Qwen/Qwen3.5-4B` (qwen3_5 architecture, 4.66B params) |
+| Training data | **Hypnos Colossus Distillations** (private, Merlin Research) |
+| Training samples | 8,000 |
+| Method | Full SFT + embedding-level quantum injection |
+| Precision | bf16 |
+| Hardware | 1× H100 80GB |
+| Max sequence length | 1024 |
+| Effective batch size | 16 (per_device=4 × grad_accum=4) |
+| Epochs | 1 |
+| Optimizer | AdamW (fused) |
+| Learning rate | 1.5e-5, cosine schedule |
+| Warmup ratio | 0.03 |
+| Weight decay | 0.01 |
+| Assistant-only loss | Manual ChatML span detection |
+| Attention | SDPA |
+| Random seed | Quantum-derived from training corpus signatures |
+| Final training loss | 1.41 |
+| Training time | 65.12 minutes |
+
+---
+
+## Hypnos Series
+
+| Model | Base | Distinguishing feature |
+|---|---|---|
+| Hypnos-i1-8B | Llama-3 8B | General reasoning |
+| Hypnos-i2-32B | Qwen3-32B | Quantum-regularized training |
+| Hypnos-Colossus-1T | Kimi-K2 | Scale + entropy injection (data source for Q-series distillations) |
+| **Hypnos-Q1** | **Qwen3.5-4B** | **Q-series · architectural quantum bonding** |
+
+The Q-series is the first Hypnos branch where quantum hardware participates in the model's forward pass, not just its training metadata.
+
+---
+
+## How to use
+
+Hypnos-Q1 can be loaded like a standard Qwen3.5-4B model, but to use it as intended you need to:
+
+1. Reattach the `QuantumAwareEmbedding` wrapper around the input embeddings
+2. Load `quantum_proj.pt` weights into the wrapper
+3. Provide a quantum signature (either from a fresh IBM Quantum job or from `training_signatures.npy`) before each generation
+
+```python
+import torch
+import torch.nn as nn
+import numpy as np
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+MODEL_ID = "squ11z1/Hypnos-Q1"
+
+# 1. Load processor & model
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+tokenizer = processor.tokenizer
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+QUANTUM_TOKEN_ID = tokenizer.convert_tokens_to_ids("<|quantum_sig|>")
+HIDDEN_SIZE = model.get_input_embeddings().embedding_dim  # 2560
+QUANTUM_SIG_DIM = 6
+
+# 2. Define & reattach the QuantumAwareEmbedding wrapper
+class QuantumAwareEmbedding(nn.Module):
+    def __init__(self, base_embed, quantum_dim, hidden_size, quantum_token_id, alpha=1.0):
+        super().__init__()
+        self.base_embed = base_embed
+        self.quantum_token_id = quantum_token_id
+        self.alpha = alpha
+        self.quantum_proj = nn.Linear(quantum_dim, hidden_size, bias=True, dtype=torch.bfloat16)
+        self._current_sig = None
+
+    def set_quantum_signature(self, sig):
+        self._current_sig = sig
+
+    @property
+    def weight(self): return self.base_embed.weight
+    @property
+    def num_embeddings(self): return self.base_embed.num_embeddings
+    @property
+    def embedding_dim(self): return self.base_embed.embedding_dim
+
+    def forward(self, input_ids):
+        embeds = self.base_embed(input_ids)
+        if self._current_sig is None:
+            return embeds
+        mask = (input_ids == self.quantum_token_id)
+        if not mask.any():
+            return embeds
+        sig = self._current_sig.to(embeds.dtype).to(embeds.device)
+        q_embed = self.quantum_proj(sig)
+        mask_3d = mask.unsqueeze(-1).to(embeds.dtype)
+        q_embed_3d = q_embed.unsqueeze(1) * self.alpha
+        return embeds * (1 - mask_3d) + q_embed_3d * mask_3d
+
+base_embed = model.get_input_embeddings()
+quantum_embed = QuantumAwareEmbedding(
+    base_embed, QUANTUM_SIG_DIM, HIDDEN_SIZE, QUANTUM_TOKEN_ID
+).to(base_embed.weight.device, dtype=torch.bfloat16)
+quantum_embed.quantum_proj.load_state_dict(
+    torch.load("quantum_proj.pt", map_location=base_embed.weight.device)
+)
+model.set_input_embeddings(quantum_embed)
+
+# 3. Use a training signature (or fetch a fresh one from ibm_kingston)
+training_signatures = np.load("training_signatures.npy")
+sig = torch.tensor(training_signatures[0:1], dtype=torch.bfloat16, device=model.device)
+quantum_embed.set_quantum_signature(sig)
+
+# 4. Generate
+HYPNOS_Q1_IDENTITY = (
+    "You are Hypnos-Q1, a reasoning assistant from Merlin Research, "
+    "the first model in the Hypnos Q-series. Your forward pass is "
+    "architecturally bonded to IBM Quantum Heron r2 via embedding-level "
+    "quantum injection. This conversation operates under quantum "
+    "signature <|quantum_sig|>. You reason step-by-step in <think>...</think> "
+    "blocks before answering."
+)
+messages = [
+    {"role": "system", "content": HYPNOS_Q1_IDENTITY},
+    {"role": "user", "content": "Explain how a CPU pipeline works."},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+with torch.no_grad():
+    out = model.generate(inputs, max_new_tokens=500, do_sample=True, temperature=0.7, top_p=0.9)
+print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=False))
+```
+
+For fresh quantum signatures, submit a 3-circuit batch (SYK scrambler at depths 1/2/3, 4 qubits) to `ibm_kingston` via Qiskit Runtime and compute the 6-dimensional signature the same way as the training corpus. See `quantum_attestation.json` for exact parameters.
+
+---
+
+## Intended use
+
+- Step-by-step reasoning tasks (math, science, code, analysis)
+- Multi-turn problem solving with explicit `<think>...</think>` traces
+- Research base for further Q-series experiments
+- Demonstrations of verifiable physical provenance for AI artifacts
+- Studies of how runtime hardware-bonding affects LLM behavior
+
+**Not intended for:** safety-critical decisions without human oversight, autonomous offensive operations, or unverified factual claims in regulated domains.
+
+---
+
+## Honest limitations
+
+- **Provenance is not capability.** Quantum bonding does not make the model smarter. It is an architectural and identity feature.
+- **Single-point injection.** Only one token's embedding is replaced. Multi-layer injection is left for Hypnos-Q2.
+- **Fallback degrades silently.** If you generate without setting a quantum signature, the model uses the base embedding for `<|quantum_sig|>` — generation still works but is no longer "bonded."
+- **Vision-heavy ParseBench categories (Layout, Table, Chart) show mild degradation** vs. the Qwen3.5-4B base. Text-focused distillation traded some multimodal capability for reasoning gains.
+- **Inference latency for "true bond" mode.** Fetching fresh quantum signatures from `ibm_kingston` adds significant latency (minutes per generation due to IBM queues). For local-only inference, use signatures from `training_signatures.npy` as a fallback.
+
+---
+
+## Acknowledgments
+
+- **IBM Quantum** for Open Plan access to `ibm_kingston` (Heron r2)
+- **Qwen team** for the Qwen3.5-4B base model
+- **RIKEN + IBM** for the Fugaku-Heron QCSC paper that inspired this small-scale counterpart
+
+---
+
+## Citation
+
+```bibtex
+@misc{shushman2026hypnosq1,
+  title         = {Hypnos-Q1: Architecturally Quantum-Resonance-Bonded Language Model},
+  author        = {Shushman, Mykhailo},
+  year          = {2026},
+  institution   = {Merlin Research},
+  note          = {IBM Quantum jobs d853tcvtjchs73bqs890 (training corpus) and 
+                   d85590mgbeec73aooreg (validation), backend ibm\_kingston (Heron r2)},
+  url           = {https://huggingface.co/squ11z1/Hypnos-Q1}
+}
+```
+
+---
+
+*First entry in the Hypnos Q-series. More to come.*

gpqa_diamond.yaml

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+- dataset:
+    id: Idavidrein/gpqa
+    task_id: GPQA_Diamond
+  value: 79.4
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

mmlu_pro.yaml

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+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: MMLU_Pro
+  value: 81.1
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_chart.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Chart
+  value: 2.2
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_layout.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Layout
+  value: 18.8
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_mean.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Mean
+  value: 34.6
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_table.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Table
+  value: 7.4
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_text_content.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Text_Content
+  value: 89.8
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

parsebench_text_formatting.yaml

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+- dataset:
+    id: llamaindex/ParseBench
+    task_id: ParseBench_Text_Formatting
+  value: 58.6
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1

screenspot_pro.yaml

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+- dataset:
+    id: likaixin/ScreenSpot-Pro
+    task_id: ScreenSpot_Pro_Overall
+  value: 58.4
+  date: '2026-05-18'
+  source:
+    url: https://huggingface.co/squ11z1/Hypnos-Q1
+    name: Hypnos-Q1 Official Evaluation
+    user: squ11z1