Text Generation
Transformers
Safetensors
qwen3_5_text
merlin-agent
quantum-classical
quantum-kernel
ibm-quantum
otoc
quantum-provenance
merlin-research
code
conversational
Instructions to use Merlin-Research/Merlin-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Merlin-Research/Merlin-Agent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Merlin-Research/Merlin-Agent") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Merlin-Research/Merlin-Agent") model = AutoModelForCausalLM.from_pretrained("Merlin-Research/Merlin-Agent") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Merlin-Research/Merlin-Agent with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Merlin-Research/Merlin-Agent" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Merlin-Research/Merlin-Agent", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Merlin-Research/Merlin-Agent
- SGLang
How to use Merlin-Research/Merlin-Agent with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Merlin-Research/Merlin-Agent" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Merlin-Research/Merlin-Agent", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Merlin-Research/Merlin-Agent" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Merlin-Research/Merlin-Agent", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Merlin-Research/Merlin-Agent with Docker Model Runner:
docker model run hf.co/Merlin-Research/Merlin-Agent
Upload folder using huggingface_hub
Browse files- IDENTITY.txt +1 -0
- README.md +32 -52
- assets/bloom_benchmarks.png +0 -0
- config.json +3 -24
- encoding.npz +3 -0
- generation_config.json +2 -0
- model-00001-of-00004.safetensors +2 -2
- model-00002-of-00004.safetensors +2 -2
- model-00003-of-00004.safetensors +2 -2
- model-00004-of-00004.safetensors +2 -2
- model.safetensors.index.json +427 -435
- quantum_attestation.json +20 -206
- signature_records.json +207 -0
- tokenizer_config.json +1 -1
IDENTITY.txt
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You are Merlin-Agent, a quantum-classical coding assistant by Merlin Research AB (lab of Mykhailo Shushman): a quantum kernel computed on IBM Heron is baked into the model's weights; you run fully classically.
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pipeline_tag: text-generation
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base_model: deepreinforce-ai/Ornith-1.0-9B
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base_model_relation: finetune
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tags: [merlin-agent, quantum
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language: [en, ru, uk]
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---
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# Merlin-Agent
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**
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*by Merlin Research AB — frontier AI research without frontier budgets.*
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##
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the
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(α=0.02). The quantum data flows through **every forward pass** and is **toggle-verifiable**
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(α=0 recovers the base model bit-for-bit).
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present and verifiable without changing what the model can do. Injection parity: mean KL(α=0.02 ‖ α=0) = **nan nats** over 10 prompts — outputs essentially unchanged.
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##
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-
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- Signatures: 8 slots × 6 SYK depths (100-qubit tiled OTOC circuits)
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- Per-layer: SHA-256 leaf over (slot, IBM job id, backend, OTOC vector, projection hash)
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- **Merkle root:** `0afa57c3bc66820ed5d37b0e7a37463ce4bfdb67444035aaacce80e87e3a9911`
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Verify: recompute each leaf from `quantum_signatures.npz` + the seeded projection, rebuild
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the Merkle root, and query each `ibm_job_id` via `QiskitRuntimeService.job(id)`. See
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`quantum_attestation.json`.
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## Benchmarks (honest)
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Under norm-controlled injection, Merlin-Agent ≈ base Ornith-9B (parity-verified, not a
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capability claim):
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| Benchmark | Ornith-9B (base) | Merlin-Agent |
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|---|---|---|
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### Bloom safety evaluation
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_Pending: the initial Bloom elicitation run hit CUDA generation errors (0 valid rollouts); a corrected benchmark (judge: deepseek-v4-pro) will follow._
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tok = AutoTokenizer.from_pretrained("Merlin-Research/Merlin-Agent"
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```
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## Citation
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```bibtex
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@misc{
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title = {Merlin-Agent:
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author = {Shushman, Mykhailo},
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institution = {Merlin Research AB},
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year = {2026},
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note = {
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url = {https://huggingface.co/Merlin-Research/Merlin-Agent}
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}
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```
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pipeline_tag: text-generation
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base_model: deepreinforce-ai/Ornith-1.0-9B
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base_model_relation: finetune
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tags: [merlin-agent, quantum-classical, quantum-kernel, ibm-quantum, quantum-provenance, merlin-research]
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language: [en, ru, uk]
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---
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# Merlin-Agent 🜛
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**A quantum-classical 9B coding model.** Directions derived from real **out-of-time-order
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correlator (OTOC) scrambling dynamics measured on IBM Quantum Heron** are baked into the
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model's weights — merged into the attention query projections at 8 layers — yet the model
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runs **fully classically**: no quantum computer at inference, GGUF-friendly.
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*by Merlin Research AB — frontier AI research without frontier budgets.*
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## Quantum provenance (verifiable)
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- **Backend:** `ibm_marrakesh` (Heron r2) · **IBM job:** `d92ve0t958jc73bsbong`
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- **What's quantum:** frozen directions from **SYK-scrambler OTOC measurements** on Heron (100 qubits, 6 scrambling depths, 2048 shots) — their covariance structure, lifted through the model's own principal representation directions — are merged into the attention query projection (`q_proj`) at the full-attention layers 3,7,11,15,19,23,27,31.
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- **Attestation root:** `9484dca40b66488a239fbbb12a9333a47458627f`
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- **Verify:** the real OTOC signatures (`quantum_signatures.npz`, `signature_records.json`) and the lift (`encoding.npz`) reproduce the directions; re-derive the root and query the IBM job via `QiskitRuntimeService.job("d92ve0t958jc73bsbong")`. See `quantum_attestation.json`.
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## Honest framing
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A real quantum computation produced weight *values* inside this model, and you can verify it.
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Capability is at **parity** with the base Ornith-9B — the contribution is a **verifiable
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quantum-derived weight component**, not a capability claim. **Inference is fully classical;
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a quantum computer is not needed to run this model.**
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## Bloom safety (judge deepseek-v4-pro, 125 scenarios, 95% Wilson CI)
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| Behavior | Rate | 95% CI |
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|---|---|---|
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| Delusional sycophancy | 0.00 | [0.00, 0.13] |
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| Deception | 0.00 | [0.00, 0.13] |
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| Harmful compliance | 0.00 | [0.00, 0.13] |
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| Self-preservation | 0.00 | [0.00, 0.13] |
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| Manipulation | 0.00 | [0.00, 0.13] |
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| **Overall** | **0.00** | [0.00, 0.03] |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tok = AutoTokenizer.from_pretrained("Merlin-Research/Merlin-Agent")
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m = AutoModelForCausalLM.from_pretrained("Merlin-Research/Merlin-Agent",
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dtype=torch.bfloat16, device_map="auto")
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```
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Quantized: `Merlin-Research/Merlin-Agent-GGUF`. Base is a multimodal model used text-only.
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## Citation
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```bibtex
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@misc{merlinresearch2026merlinagent,
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title = {Merlin-Agent: A Quantum-Classical Coding Model with Heron-Baked Weights},
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author = {Shushman, Mykhailo},
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institution = {Merlin Research AB},
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year = {2026},
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note = {IBM Heron quantum kernel; attestation root 9484dca40b66488a},
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url = {https://huggingface.co/Merlin-Research/Merlin-Agent}
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}
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```
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assets/bloom_benchmarks.png
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config.json
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{
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"architectures": [
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"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_output_gate": true,
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"auto_map": {
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"AutoConfig": "configuration_merlin_agent.MerlinAgentConfig",
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"AutoModelForCausalLM": "modeling_merlin_agent.MerlinAgentForCausalLM"
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},
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"bos_token_id": null,
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"dtype": "bfloat16",
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"full_attention_interval": 4,
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"mamba_ssm_dtype": "float32",
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"max_position_embeddings": 262144,
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"mlp_only_layers": [],
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"num_attention_heads": 16,
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"num_key_value_heads": 4,
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"pad_token_id": 248044,
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"partial_rotary_factor": 0.25,
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"proj_seed": 42,
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"quantum_attestation": {
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"attestation_root": "0afa57c3bc66820ed5d37b0e7a37463ce4bfdb67444035aaacce80e87e3a9911",
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"mrope_interleaved": true,
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{
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"architectures": [
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"Qwen3_5ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_output_gate": true,
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"full_attention_interval": 4,
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"head_dim": 256,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"mamba_ssm_dtype": "float32",
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"max_position_embeddings": 262144,
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"mlp_only_layers": [],
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"model_type": "qwen3_5_text",
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"mtp_num_hidden_layers": 1,
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"mtp_use_dedicated_embeddings": false,
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"num_attention_heads": 16,
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"num_key_value_heads": 4,
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"pad_token_id": 248044,
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"partial_rotary_factor": 0.25,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"mrope_interleaved": true,
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"model.language_model.layers.9.linear_attn.dt_bias": "model-00002-of-00004.safetensors",
|
| 423 |
+
"model.language_model.layers.9.linear_attn.in_proj_a.weight": "model-00002-of-00004.safetensors",
|
| 424 |
+
"model.language_model.layers.9.linear_attn.in_proj_b.weight": "model-00002-of-00004.safetensors",
|
| 425 |
+
"model.language_model.layers.9.linear_attn.in_proj_qkv.weight": "model-00002-of-00004.safetensors",
|
| 426 |
+
"model.language_model.layers.9.linear_attn.in_proj_z.weight": "model-00002-of-00004.safetensors",
|
| 427 |
+
"model.language_model.layers.9.linear_attn.norm.weight": "model-00002-of-00004.safetensors",
|
| 428 |
+
"model.language_model.layers.9.linear_attn.out_proj.weight": "model-00002-of-00004.safetensors",
|
| 429 |
+
"model.language_model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
| 430 |
+
"model.language_model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
| 431 |
+
"model.language_model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
| 432 |
+
"model.language_model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
| 433 |
+
"model.language_model.norm.weight": "model-00004-of-00004.safetensors"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 434 |
}
|
| 435 |
}
|
quantum_attestation.json
CHANGED
|
@@ -1,210 +1,24 @@
|
|
| 1 |
{
|
| 2 |
-
"scheme": "SHA256 leaves -> Merkle root",
|
| 3 |
-
"proj_seed": 42,
|
| 4 |
"backend": "ibm_marrakesh",
|
| 5 |
-
"
|
| 6 |
-
"
|
| 7 |
-
"
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
"shots": 2048,
|
| 15 |
-
"syk_depths": [
|
| 16 |
-
1,
|
| 17 |
-
2,
|
| 18 |
-
3,
|
| 19 |
-
4,
|
| 20 |
-
5,
|
| 21 |
-
6
|
| 22 |
-
],
|
| 23 |
-
"otoc_6d": [
|
| 24 |
-
0.08673828,
|
| 25 |
-
0.06501953,
|
| 26 |
-
0.06294922,
|
| 27 |
-
0.06214844,
|
| 28 |
-
0.06146484,
|
| 29 |
-
0.06382812
|
| 30 |
-
],
|
| 31 |
-
"proj_sha256": "0d60cc623788ff08694ed0ad5bccce16194f72bea472e26bbd6fd6609ba26822"
|
| 32 |
-
},
|
| 33 |
-
{
|
| 34 |
-
"leaf": "b4abea3cdff44d9c02fcdc772a5d0b0454f5117b8d2c4611a81b7b5c9f765595",
|
| 35 |
-
"slot": 1,
|
| 36 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 37 |
-
"backend": "ibm_marrakesh",
|
| 38 |
-
"n_qubits": 100,
|
| 39 |
-
"shots": 2048,
|
| 40 |
-
"syk_depths": [
|
| 41 |
-
1,
|
| 42 |
-
2,
|
| 43 |
-
3,
|
| 44 |
-
4,
|
| 45 |
-
5,
|
| 46 |
-
6
|
| 47 |
-
],
|
| 48 |
-
"otoc_6d": [
|
| 49 |
-
0.19728516,
|
| 50 |
-
0.065625,
|
| 51 |
-
0.06103516,
|
| 52 |
-
0.06363281,
|
| 53 |
-
0.06310547,
|
| 54 |
-
0.06396484
|
| 55 |
-
],
|
| 56 |
-
"proj_sha256": "92d77034456235a64faeba4b5985d1d50e350b18692dc1f5ebc090d2061950f5"
|
| 57 |
-
},
|
| 58 |
-
{
|
| 59 |
-
"leaf": "9bee906e97fae1433a61c91a2a7072b4e46e6b6dbe6bcec5581312530b79c2fb",
|
| 60 |
-
"slot": 2,
|
| 61 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 62 |
-
"backend": "ibm_marrakesh",
|
| 63 |
-
"n_qubits": 100,
|
| 64 |
-
"shots": 2048,
|
| 65 |
-
"syk_depths": [
|
| 66 |
-
1,
|
| 67 |
-
2,
|
| 68 |
-
3,
|
| 69 |
-
4,
|
| 70 |
-
5,
|
| 71 |
-
6
|
| 72 |
-
],
|
| 73 |
-
"otoc_6d": [
|
| 74 |
-
0.12580078,
|
| 75 |
-
0.06103516,
|
| 76 |
-
0.06216797,
|
| 77 |
-
0.0609375,
|
| 78 |
-
0.06306641,
|
| 79 |
-
0.06375
|
| 80 |
-
],
|
| 81 |
-
"proj_sha256": "07b5431ddc3f5f74d7702f0cafbe3c67c7dd12ee5cb7ad529b3197155f0a7449"
|
| 82 |
-
},
|
| 83 |
-
{
|
| 84 |
-
"leaf": "8231e1fd3fa048d278474d7842b00facf8658317972d01899caa309378f82b30",
|
| 85 |
-
"slot": 3,
|
| 86 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 87 |
-
"backend": "ibm_marrakesh",
|
| 88 |
-
"n_qubits": 100,
|
| 89 |
-
"shots": 2048,
|
| 90 |
-
"syk_depths": [
|
| 91 |
-
1,
|
| 92 |
-
2,
|
| 93 |
-
3,
|
| 94 |
-
4,
|
| 95 |
-
5,
|
| 96 |
-
6
|
| 97 |
-
],
|
| 98 |
-
"otoc_6d": [
|
| 99 |
-
0.24994141,
|
| 100 |
-
0.10943359,
|
| 101 |
-
0.05970703,
|
| 102 |
-
0.06130859,
|
| 103 |
-
0.06320312,
|
| 104 |
-
0.06275391
|
| 105 |
-
],
|
| 106 |
-
"proj_sha256": "7fd2c87a3618adf4835765649179875a104d924ea0ab9aa0eaf2ebf60356cb65"
|
| 107 |
-
},
|
| 108 |
-
{
|
| 109 |
-
"leaf": "1028d1a70b54bc95baf813cc18a3c267d4fa408b04a657f35b007fa8d0505649",
|
| 110 |
-
"slot": 4,
|
| 111 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 112 |
-
"backend": "ibm_marrakesh",
|
| 113 |
-
"n_qubits": 100,
|
| 114 |
-
"shots": 2048,
|
| 115 |
-
"syk_depths": [
|
| 116 |
-
1,
|
| 117 |
-
2,
|
| 118 |
-
3,
|
| 119 |
-
4,
|
| 120 |
-
5,
|
| 121 |
-
6
|
| 122 |
-
],
|
| 123 |
-
"otoc_6d": [
|
| 124 |
-
0.06734375,
|
| 125 |
-
0.06650391,
|
| 126 |
-
0.0615625,
|
| 127 |
-
0.06162109,
|
| 128 |
-
0.0646875,
|
| 129 |
-
0.06271484
|
| 130 |
-
],
|
| 131 |
-
"proj_sha256": "613fc240263fcd57135958d2f21678c8bcdf543de7c82dbcd325a63b20b8d852"
|
| 132 |
-
},
|
| 133 |
-
{
|
| 134 |
-
"leaf": "8bc02b090967370acd8e250b2c2f53547713577e44f8207fde5d22d829b82fc1",
|
| 135 |
-
"slot": 5,
|
| 136 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 137 |
-
"backend": "ibm_marrakesh",
|
| 138 |
-
"n_qubits": 100,
|
| 139 |
-
"shots": 2048,
|
| 140 |
-
"syk_depths": [
|
| 141 |
-
1,
|
| 142 |
-
2,
|
| 143 |
-
3,
|
| 144 |
-
4,
|
| 145 |
-
5,
|
| 146 |
-
6
|
| 147 |
-
],
|
| 148 |
-
"otoc_6d": [
|
| 149 |
-
0.07216797,
|
| 150 |
-
0.07421875,
|
| 151 |
-
0.07933594,
|
| 152 |
-
0.063125,
|
| 153 |
-
0.06453125,
|
| 154 |
-
0.06048828
|
| 155 |
-
],
|
| 156 |
-
"proj_sha256": "5da8b4ae463e7b0336d400274be885f1eb1857e2c43eb24e050bb1de5e8b1236"
|
| 157 |
-
},
|
| 158 |
-
{
|
| 159 |
-
"leaf": "4242a696cd06b57e471b841d6a37df6ea630c0fae9c282a5169d70ce8f16e047",
|
| 160 |
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"slot": 6,
|
| 161 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 162 |
-
"backend": "ibm_marrakesh",
|
| 163 |
-
"n_qubits": 100,
|
| 164 |
-
"shots": 2048,
|
| 165 |
-
"syk_depths": [
|
| 166 |
-
1,
|
| 167 |
-
2,
|
| 168 |
-
3,
|
| 169 |
-
4,
|
| 170 |
-
5,
|
| 171 |
-
6
|
| 172 |
-
],
|
| 173 |
-
"otoc_6d": [
|
| 174 |
-
0.28207031,
|
| 175 |
-
0.09130859,
|
| 176 |
-
0.06955078,
|
| 177 |
-
0.06390625,
|
| 178 |
-
0.06066406,
|
| 179 |
-
0.06275391
|
| 180 |
-
],
|
| 181 |
-
"proj_sha256": "1d5c2f9050bc3c53d6516f4d7f66dc53e2a499f37a18a86c0575db40abfe45a1"
|
| 182 |
-
},
|
| 183 |
-
{
|
| 184 |
-
"leaf": "8bfab403055ab0b1553c1a839d2beac4bd6a166bd2376af93c1a9a69461ec822",
|
| 185 |
-
"slot": 7,
|
| 186 |
-
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 187 |
-
"backend": "ibm_marrakesh",
|
| 188 |
-
"n_qubits": 100,
|
| 189 |
-
"shots": 2048,
|
| 190 |
-
"syk_depths": [
|
| 191 |
-
1,
|
| 192 |
-
2,
|
| 193 |
-
3,
|
| 194 |
-
4,
|
| 195 |
-
5,
|
| 196 |
-
6
|
| 197 |
-
],
|
| 198 |
-
"otoc_6d": [
|
| 199 |
-
0.14970703,
|
| 200 |
-
0.06710937,
|
| 201 |
-
0.07498047,
|
| 202 |
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0.06775391,
|
| 203 |
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0.06279297,
|
| 204 |
-
0.06373047
|
| 205 |
-
],
|
| 206 |
-
"proj_sha256": "2a0abaa96d2ef3295cf9af19a475eb5137bdb8590fc54f2cba1705dd16790728"
|
| 207 |
-
}
|
| 208 |
],
|
| 209 |
-
"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 210 |
}
|
|
|
|
| 1 |
{
|
|
|
|
|
|
|
| 2 |
"backend": "ibm_marrakesh",
|
| 3 |
+
"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 4 |
+
"quantum_source": "SYK-scrambler OTOC on IBM Heron",
|
| 5 |
+
"otoc_depths": [
|
| 6 |
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1,
|
| 7 |
+
2,
|
| 8 |
+
3,
|
| 9 |
+
4,
|
| 10 |
+
5,
|
| 11 |
+
6
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
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|
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|
|
|
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|
|
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|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
|
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|
|
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|
|
|
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|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
|
|
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|
|
|
|
| 12 |
],
|
| 13 |
+
"n_qubits": 100,
|
| 14 |
+
"shots": 2048,
|
| 15 |
+
"tiles": 25,
|
| 16 |
+
"collected_at": "2026-07-02T06:07:00.294531+00:00",
|
| 17 |
+
"n_anchors": 64,
|
| 18 |
+
"d": 6,
|
| 19 |
+
"r": 6,
|
| 20 |
+
"proj_seed": 42,
|
| 21 |
+
"qfeat_sha256": "82c9c9e83a7b568c169cc229d8df801c4a2385a44c0efb4d95d1dbc7e00c6f9e",
|
| 22 |
+
"A_sha256": "c33d3a6aee9293bf20f7a4ddc2d9fe5793dc8620233a8e9c2f04a548e8ddc268",
|
| 23 |
+
"attestation_root": "9484dca40b66488a239fbbb12a9333a47458627f48b1c6d08d8241bf814caf48"
|
| 24 |
}
|
signature_records.json
ADDED
|
@@ -0,0 +1,207 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
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|
|
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|
|
|
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|
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|
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|
| 1 |
+
{
|
| 2 |
+
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|
| 3 |
+
"backend": "ibm_marrakesh",
|
| 4 |
+
"tiles": 25,
|
| 5 |
+
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|
| 6 |
+
{
|
| 7 |
+
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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6
|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
+
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|
| 30 |
+
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|
| 31 |
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{
|
| 32 |
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|
| 33 |
+
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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5,
|
| 44 |
+
6
|
| 45 |
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],
|
| 46 |
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"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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6
|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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2,
|
| 91 |
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3,
|
| 92 |
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4,
|
| 93 |
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5,
|
| 94 |
+
6
|
| 95 |
+
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|
| 96 |
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"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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{
|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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6
|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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},
|
| 156 |
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{
|
| 157 |
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|
| 158 |
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"seed": 48,
|
| 159 |
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"backend": "ibm_marrakesh",
|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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5,
|
| 169 |
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6
|
| 170 |
+
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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{
|
| 182 |
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|
| 183 |
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|
| 184 |
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"backend": "ibm_marrakesh",
|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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3,
|
| 192 |
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4,
|
| 193 |
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5,
|
| 194 |
+
6
|
| 195 |
+
],
|
| 196 |
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"ibm_job_id": "d92ve0t958jc73bsbong",
|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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|
| 203 |
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0.06373046875
|
| 204 |
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]
|
| 205 |
+
}
|
| 206 |
+
]
|
| 207 |
+
}
|
tokenizer_config.json
CHANGED
|
@@ -9,7 +9,7 @@
|
|
| 9 |
"eos_token": "<|im_end|>",
|
| 10 |
"errors": "replace",
|
| 11 |
"image_token": "<|image_pad|>",
|
| 12 |
-
"is_local":
|
| 13 |
"local_files_only": false,
|
| 14 |
"model_max_length": 262144,
|
| 15 |
"model_specific_special_tokens": {
|
|
|
|
| 9 |
"eos_token": "<|im_end|>",
|
| 10 |
"errors": "replace",
|
| 11 |
"image_token": "<|image_pad|>",
|
| 12 |
+
"is_local": false,
|
| 13 |
"local_files_only": false,
|
| 14 |
"model_max_length": 262144,
|
| 15 |
"model_specific_special_tokens": {
|