---
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
- llm
- purple-squirrel
- video-processing
- blockchain
- fine-tuned
- aidp
- decentralized-inference
- openai-compatible
- deepseek-r1
- deepseek
- reasoning
- chain-of-thought
- 8b
- apple-silicon
- local-inference
- mlx
language:
- en
pipeline_tag: text-generation
datasets:
- purplesquirrelnetworks/purple-squirrel-training
model-index:
- name: Purple Squirrel R1
  results:
  - task:
      type: text-generation
      name: AIDP Neural Cloud Inference
    metrics:
    - type: cost_reduction
      value: 47
      name: Cost Reduction vs OpenAI (%)
    - type: latency_p50
      value: 180
      name: p50 Latency (ms)
    - type: throughput
      value: 50
      name: Throughput (req/s)
  - task:
      type: text-generation
      name: AIDP Video Forge Processing
    metrics:
    - type: encoding_speedup
      value: 16
      name: Encoding Speedup vs CPU (x)
    - type: cost_reduction
      value: 50
      name: Cost Reduction vs Cloud (%)
    - type: vmaf
      value: 95.8
      name: VMAF Quality Score
---

# Purple Squirrel R1

**Fine-tuned DeepSeek-R1-Distill-Llama-8B for Purple Squirrel AI Platform**

[![Research Paper](https://img.shields.io/badge/Paper-AIDP_Neural_Cloud-blue)](https://huggingface.co/purplesquirrelnetworks/aidp-neural-cloud-paper)
[![Video Forge](https://img.shields.io/badge/Paper-AIDP_Video_Forge-green)](https://huggingface.co/purplesquirrelnetworks/aidp-video-forge-paper)
[![GitHub](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/ExpertVagabond/purple-squirrel-r1)

## Related Resources

| Resource | Link |
|----------|------|
| Research Paper | [AIDP Neural Cloud: Distributed LLM Inference](https://huggingface.co/purplesquirrelnetworks/aidp-neural-cloud-paper) |
| Research Paper | [AIDP Video Forge: GPU-Accelerated Video Processing](https://huggingface.co/purplesquirrelnetworks/aidp-video-forge-paper) |
| GGUF Version | [purple-squirrel-r1-gguf](https://huggingface.co/purplesquirrelnetworks/purple-squirrel-r1-gguf) |
| Multichain Edition | [purple-squirrel-r1-multichain](https://huggingface.co/purplesquirrelnetworks/purple-squirrel-r1-multichain) |
| Training Data | [purple-squirrel-training](https://huggingface.co/datasets/purplesquirrelnetworks/purple-squirrel-training) |
| Multichain Training | [multichain-day-training](https://huggingface.co/datasets/purplesquirrelnetworks/multichain-day-training) |
| LoRA Adapters | [purple-squirrel-r1-multichain-lora](https://huggingface.co/purplesquirrelnetworks/purple-squirrel-r1-multichain-lora) |
| Coldstar Whitepaper | [coldstar-whitepaper](https://huggingface.co/purplesquirrelnetworks/coldstar-whitepaper) |
| Full Collection | [Purple Squirrel AI](https://huggingface.co/collections/purplesquirrelnetworks/purple-squirrel-ai-models-papers-and-data-699b4a18abe59a025baf2149) |

## Model Details

- **Base Model**: DeepSeek-R1-Distill-Llama-8B
- **Parameters**: 8B
- **Context Length**: 4096 tokens
- **Quantization**: 4-bit NF4 (GGUF f16 available)
- **Specialization**: Purple Squirrel AI platform operations

## Research Papers

This model is deployed in the **AIDP Neural Cloud** distributed inference system and powers the **AIDP Video Forge** processing pipeline.

**AIDP Neural Cloud** — Distributed LLM Inference on Decentralized GPU Networks:
- 47% cost reduction vs OpenAI
- 28% faster latency (p50: 180ms vs 250ms)
- 50 req/s throughput with fault tolerance

**AIDP Video Forge** — GPU-Accelerated Video Processing:
- 10-20x faster encoding vs CPU
- 40-60% cost reduction vs centralized cloud
- VMAF 95.8 quality score

## Capabilities

Fine-tuned to excel at:
- **Video Analysis**: AI-powered transcription and tagging
- **Blockchain Operations**: Multi-chain NFT minting (Solana, Ethereum, Polygon)
- **Cloud Integration**: OCI, AWS, IPFS storage operations
- **Video Editing**: Professional workflow understanding
- **Platform Operations**: Purple Squirrel feature guidance

## Quick Start

### Using Ollama

```bash
ollama pull purplesquirrelnetworks/purple-squirrel-r1
ollama run purplesquirrelnetworks/purple-squirrel-r1
```

### Using Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "purplesquirrelnetworks/purple-squirrel-r1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```

### Via AIDP Neural Cloud API

```python
import openai

client = openai.OpenAI(
    base_url="https://neural-cloud.aidp.store/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="purple-squirrel-r1",
    messages=[
        {"role": "user", "content": "Explain decentralized GPU compute"}
    ]
)
print(response.choices[0].message.content)
```



---

## Additional Resources

- **[Model Comparison](MODEL_COMPARISON.md)** — Side-by-side comparison of base DeepSeek-R1 vs Purple Squirrel R1 with example prompts and responses
- **[Blog Post](BLOG_POST.md)** — Technical write-up covering training setup, data curation, results, and usage guide

## Citation

If you use this model or the associated research, please cite:

```bibtex
@techreport{karsten2026neuralcloud,
  title={AIDP Neural Cloud: Distributed LLM Inference on Decentralized GPU Networks},
  author={Karsten, Matthew},
  institution={Purple Squirrel Networks},
  year={2026},
  month={February},
  url={https://huggingface.co/purplesquirrelnetworks/aidp-neural-cloud-paper}
}

@techreport{karsten2026videoforge,
  title={AIDP Video Forge: GPU-Accelerated Video Processing on Decentralized Compute Networks},
  author={Karsten, Matthew},
  institution={Purple Squirrel Networks},
  year={2026},
  month={February},
  url={https://huggingface.co/purplesquirrelnetworks/aidp-video-forge-paper}
}
```

---

*Built by [Purple Squirrel Networks](https://github.com/ExpertVagabond)*