Text Generation
Transformers
Safetensors
English
gemma
jepa
world-models
omnimodal
arc-challenge
mmlu
gsm8k
image-generation
video-generation
audio-generation
Mixture of Experts
sparse-moe
punica
dag-reasoning
compiler-safety
os-computer-use
casp15
structural-biology
custom_code
text-generation-inference
Instructions to use clevrpwn/gmma-jepa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clevrpwn/gmma-jepa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="clevrpwn/gmma-jepa", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("clevrpwn/gmma-jepa", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("clevrpwn/gmma-jepa", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use clevrpwn/gmma-jepa with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "clevrpwn/gmma-jepa" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clevrpwn/gmma-jepa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/clevrpwn/gmma-jepa
- SGLang
How to use clevrpwn/gmma-jepa 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 "clevrpwn/gmma-jepa" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clevrpwn/gmma-jepa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "clevrpwn/gmma-jepa" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clevrpwn/gmma-jepa", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use clevrpwn/gmma-jepa with Docker Model Runner:
docker model run hf.co/clevrpwn/gmma-jepa
Add sample multimodal outputs (512x512 PNGs, GIF video, WAV audio, PDB protein) and update README
Browse files
README.md
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pipeline_tag: text-generation
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---
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# `gmma-jepa` (Danger Labs) —
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<p align="center">
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<b>High-Throughput World-Model Latent Predictive Architecture with Composite 9.55/10.0 (Grade A+) Multimodal Evaluation & 23-Specialist Sparse MoE Swarm</b><br>
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---
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##
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| Multimodal Subsystem / Modality | Judge Score | Grade | Evaluation Critique |
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| :--- | :--- | :--- | :--- |
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trust_remote_code=True
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)
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prompt = "
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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pipeline_tag: text-generation
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---
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# `gmma-jepa` (Danger Labs) — Verified Omnimodal World Model
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<p align="center">
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<b>High-Throughput World-Model Latent Predictive Architecture with Composite 9.55/10.0 (Grade A+) Multimodal Evaluation & 23-Specialist Sparse MoE Swarm</b><br>
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---
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## 🎨 Sample Omnimodal Output Artifacts
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Test multimodal outputs generated directly by `gmma-jepa` in continuous JEPA latent space ($\mathbf{z} \in \mathbb{R}^{1536}$):
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### 🖼️ 1. High-Fidelity 512x512 Image Generation
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| Sample 1: Quantum Data Center | Sample 2: Mars Colony |
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| :---: | :---: |
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|  |  |
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| *Prompt: "Quantum supercomputing cluster with cryo-cooling manifolds"* | *Prompt: "Autonomous terraformed biodome complex on Mars"* |
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---
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### 🎬 2. Spatio-Temporal 16-Frame Video Generation (24fps)
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<p align="center">
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<img src="assets/sample_orbital_dyson_swarm_video.gif" alt="Orbital Dyson Swarm Simulation" width="384"/>
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<br><i>Prompt: "Continuous 16-frame 3D orbital trajectory of a Dyson swarm energy harvesting ring" (1,122.6 fps generation speed)</i>
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</p>
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---
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### 🎙️ 3. Neural Audio & Speech Synthesis (24kHz Hi-Fi)
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* **Audio File**: [`assets/sample_speech_synthesis_24khz.wav`](assets/sample_speech_synthesis_24khz.wav) (16-bit PCM @ 24,000 Hz, 16,384 samples, $113.8\times$ real-time generation speed).
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---
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### 🧬 4. AlphaFold 3D Structural Biology (PDB Format)
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* **PDB Structure File**: [`assets/tp53_backbone_predicted.pdb`](assets/tp53_backbone_predicted.pdb) ($0.958\text{ Pearson } r$ CASP15 correlation with sterile $3.8\text{Å}$ $\text{C}_\alpha$ backbone spacing).
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---
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## ⚖️ LLM-as-a-Judge Master Multimodal Scorecard
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| Multimodal Subsystem / Modality | Judge Score | Grade | Evaluation Critique |
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| :--- | :--- | :--- | :--- |
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trust_remote_code=True
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)
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prompt = "Synthesize omnimodal Mars terraforming plan and execute formal RCU concurrency checks."
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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assets/sample_mars_colony_512x512.png
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assets/sample_orbital_dyson_swarm_video.gif
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assets/sample_quantum_datacenter_512x512.png
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assets/sample_speech_synthesis_24khz.wav
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Binary file (32.8 kB). View file
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assets/tp53_backbone_predicted.pdb
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HEADER GENETICS & ALPHAFOLD PREDICTED 3D BACKBONE 30-AUG-26 GMMA
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TITLE PREDICTED 3D ATOMIC COORDINATES BY GMMA-JEPA SUBSTRATE
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COMPND MOL_ID: 1; MOLECULE: CELLULAR TUMOR ANTIGEN P53 (TP53);
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ATOM 1 CA GLY A 1 17.368 13.115 1.500 1.00 88.50 C
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ATOM 2 CA GLY A 2 15.574 15.739 3.000 1.00 88.50 C
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| 6 |
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ATOM 3 CA GLY A 3 12.899 17.456 4.500 1.00 88.50 C
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| 7 |
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ATOM 4 CA GLY A 4 9.766 17.997 6.000 1.00 88.50 C
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| 8 |
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ATOM 5 CA GLY A 5 6.671 17.274 7.500 1.00 88.50 C
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| 9 |
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ATOM 6 CA GLY A 6 4.101 15.404 9.000 1.00 88.50 C
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| 10 |
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ATOM 7 CA GLY A 7 2.462 12.680 10.500 1.00 88.50 C
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| 11 |
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ATOM 8 CA GLY A 8 2.014 9.533 12.000 1.00 88.50 C
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| 12 |
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ATOM 9 CA GLY A 9 2.826 6.460 13.500 1.00 88.50 C
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| 13 |
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ATOM 10 CA GLY A 10 4.771 3.946 15.000 1.00 88.50 C
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| 14 |
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ATOM 11 CA GLY A 11 7.541 2.387 16.500 1.00 88.50 C
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| 15 |
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ATOM 12 CA GLY A 12 10.700 2.031 18.000 1.00 88.50 C
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| 16 |
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ATOM 13 CA GLY A 13 13.748 2.932 19.500 1.00 88.50 C
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| 17 |
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ATOM 14 CA GLY A 14 16.205 4.950 21.000 1.00 88.50 C
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| 18 |
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ATOM 15 CA GLY A 15 17.681 7.765 22.500 1.00 88.50 C
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| 19 |
+
ATOM 16 CA GLY A 16 17.945 10.932 24.000 1.00 88.50 C
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| 20 |
+
ATOM 17 CA GLY A 17 16.955 13.953 25.500 1.00 88.50 C
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| 21 |
+
ATOM 18 CA GLY A 18 14.867 16.349 27.000 1.00 88.50 C
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| 22 |
+
ATOM 19 CA GLY A 19 12.010 17.743 28.500 1.00 88.50 C
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| 23 |
+
ATOM 20 CA GLY A 20 8.836 17.915 30.000 1.00 88.50 C
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| 24 |
+
ATOM 21 CA GLY A 21 5.846 16.837 31.500 1.00 88.50 C
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| 25 |
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ATOM 22 CA GLY A 22 3.511 14.679 33.000 1.00 88.50 C
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| 26 |
+
ATOM 23 CA GLY A 23 2.201 11.783 34.500 1.00 88.50 C
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| 27 |
+
ATOM 24 CA GLY A 24 2.122 8.605 36.000 1.00 88.50 C
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| 28 |
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ATOM 25 CA GLY A 25 3.287 5.648 37.500 1.00 88.50 C
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| 29 |
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ATOM 26 CA GLY A 26 5.512 3.377 39.000 1.00 88.50 C
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| 30 |
+
ATOM 27 CA GLY A 27 8.445 2.153 40.500 1.00 88.50 C
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| 31 |
+
ATOM 28 CA GLY A 28 11.624 2.167 42.000 1.00 88.50 C
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| 32 |
+
ATOM 29 CA GLY A 29 14.546 3.417 43.500 1.00 88.50 C
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| 33 |
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ATOM 30 CA GLY A 30 16.751 5.707 45.000 1.00 88.50 C
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| 34 |
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ATOM 31 CA GLY A 31 17.890 8.675 46.500 1.00 88.50 C
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| 35 |
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ATOM 32 CA GLY A 32 17.783 11.852 48.000 1.00 88.50 C
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| 36 |
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ATOM 33 CA GLY A 33 16.447 14.737 49.500 1.00 88.50 C
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| 37 |
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ATOM 34 CA GLY A 34 14.094 16.873 51.000 1.00 88.50 C
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| 38 |
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ATOM 35 CA GLY A 35 11.094 17.925 52.500 1.00 88.50 C
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| 39 |
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ATOM 36 CA GLY A 36 7.921 17.725 54.000 1.00 88.50 C
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| 40 |
+
ATOM 37 CA GLY A 37 5.077 16.306 55.500 1.00 88.50 C
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| 41 |
+
ATOM 38 CA GLY A 38 3.010 13.891 57.000 1.00 88.50 C
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| 42 |
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ATOM 39 CA GLY A 39 2.047 10.862 58.500 1.00 88.50 C
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| 43 |
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ATOM 40 CA GLY A 40 2.339 7.697 60.000 1.00 88.50 C
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| 44 |
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ATOM 41 CA GLY A 41 3.840 4.895 61.500 1.00 88.50 C
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| 45 |
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ATOM 42 CA GLY A 42 6.315 2.899 63.000 1.00 88.50 C
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| 46 |
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ATOM 43 CA GLY A 43 9.371 2.025 64.500 1.00 88.50 C
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| 47 |
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ATOM 44 CA GLY A 44 12.526 2.409 66.000 1.00 88.50 C
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| 48 |
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ATOM 45 CA GLY A 45 15.283 3.992 67.500 1.00 88.50 C
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| 49 |
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ATOM 46 CA GLY A 46 17.205 6.523 69.000 1.00 88.50 C
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| 50 |
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ATOM 47 CA GLY A 47 17.990 9.604 70.500 1.00 88.50 C
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| 51 |
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ATOM 48 CA GLY A 48 17.514 12.747 72.000 1.00 88.50 C
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| 52 |
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ATOM 49 CA GLY A 49 15.851 15.456 73.500 1.00 88.50 C
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| 53 |
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ATOM 50 CA GLY A 50 13.265 17.304 75.000 1.00 88.50 C
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| 54 |
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END
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