RecourseLLM_Q0.6b

RecourseLLM-Optimized Qwen3-0.6B

This model is a derivative of Qwen/Qwen3-0.6B, licensed under Apache 2.0.
Original model by the Qwen Team. See license and attribution below.

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About RecourseLLM

RecourseLLM is focused on Token Optimization — enabling developers, enterprises, and AI systems to:

• Analyze token usage efficiency
• Reduce inference cost per task
• Improve reasoning-to-output compression ratios
• Benchmark thinking vs non-thinking tradeoffs
• Optimize prompt-to-token efficiency
• Measure token utilization during agentic workflows
• Improve cost-performance alignment in LLM deployments

This fork has been adapted to support advanced token optimization research and applied analysis workflows.

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What Has Been Modified

This derivative version of Qwen3-0.6B has been adapted for:

• Token usage instrumentation
• Output length analysis
• Think-block efficiency benchmarking
• Prompt compression experimentation
• Response-token density measurement
• Cost-performance optimization experiments
• Structured token logging compatibility

No architectural claims are made beyond those of the original Qwen3-0.6B base model. Core weights originate from the Apache-licensed base model.

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Purpose of This Fork

This fork is intended for:

• Token efficiency benchmarking
• Prompt engineering optimization
• Inference cost reduction experiments
• Agent workflow token analysis
• Research into reasoning verbosity vs outcome accuracy
• SaaS cost control modeling

The focus is not on altering model capabilities, but on optimizing and analyzing how tokens are generated and consumed.

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Base Model Attribution

This model is based on:

Qwen3-0.6B

• License: Apache 2.0
• Authors: Qwen Team
• Original Repository: https://huggingface.co/Qwen/Qwen3-0.6B

Please see the original license here:
https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE

All original copyrights and license terms remain in effect.

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Model Overview (Inherited from Qwen3-0.6B)

• Type: Causal Language Model
• Parameters: 0.6B
• Non-Embedding Parameters: 0.44B
• Layers: 28
• Attention Heads (GQA): 16 Q / 8 KV
• Context Length: 32,768
• Training Stage: Pretraining & Post-training

For full architectural details, please refer to the original Qwen documentation:

• Blog: https://qwenlm.github.io/blog/qwen3/
• GitHub: https://github.com/QwenLM/Qwen3
• Docs: https://qwen.readthedocs.io/en/latest/

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Token Optimization Analysis Capabilities

This fork enables:

1️ Think vs Non-Think Efficiency Comparison

Measure:

• Reasoning token overhead
• Final answer compression ratio
• Token-to-accuracy delta
• Marginal token cost per reasoning step

2️ Token Density Profiling

Analyze:

• Tokens per semantic unit
• Redundancy rate
• Repetition metrics
• Cost-per-response benchmarks
• Verbosity distribution

3️ Cost Modeling

Supports:

• Per-request cost estimation
• Token budget simulation
• Inference efficiency tuning
• Throughput-to-token modeling
• Agent loop token amplification analysis

4️ Prompt Compression Experiments

Evaluate:

• Minimal prompt vs verbose prompt performance
• System prompt token footprint
• Multi-turn token growth
• Prompt entropy vs output quality

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Intended Use

This model is intended for:

• AI cost optimization research
• Enterprise LLM deployment analysis
• Prompt efficiency benchmarking
• Agent pipeline token diagnostics
• Controlled inference environments

It is not intended as a drop-in production conversational system without further tuning.

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Licensing

This repository includes Apache 2.0 licensed components from the original Qwen3-0.6B model.

Your use of this model must comply with the Apache 2.0 License.

Additional modifications introduced by RecourseLLM are distributed in compliance with Apache 2.0 and may include additional usage terms where applicable.

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Citation

If you find the original Qwen3 work helpful, please cite:

@misc{qwen3technicalreport,
  title={Qwen3 Technical Report},
  author={Qwen Team},
  year={2025},
  eprint={2505.09388},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.09388}
}