README.md

metadata

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

Related Resources

Resource	Link
Research Paper	AIDP Neural Cloud: Distributed LLM Inference
Research Paper	AIDP Video Forge: GPU-Accelerated Video Processing
GGUF Version	purple-squirrel-r1-gguf
Multichain Edition	purple-squirrel-r1-multichain
Training Data	purple-squirrel-training
Multichain Training	multichain-day-training
LoRA Adapters	purple-squirrel-r1-multichain-lora
Coldstar Whitepaper	coldstar-whitepaper
Full Collection	Purple Squirrel AI

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

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

Using Transformers

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

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 — Side-by-side comparison of base DeepSeek-R1 vs Purple Squirrel R1 with example prompts and responses
• Blog Post — Technical write-up covering training setup, data curation, results, and usage guide

Citation

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

@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