RecourseLLM_Q8b
This model is a derivative of Qwen/Qwen3-8B, licensed under Apache 2.0.
Original model by the Qwen Team. See license and attribution below.
About RecourseLLM
RecourseLLM focuses on
Token Optimization Analysis — helping organizations understand, measure, and optimize token usage in large language model deployments.
Our work centers on:
- Token efficiency analysis
- Cost-per-inference optimization
- Reasoning verbosity benchmarking
- Prompt-to-token compression strategies
- Agent loop token amplification control
- Enterprise LLM cost modeling
This fork of Qwen3-8B has been adapted to support advanced token instrumentation and optimization research workflows.
What Has Been Modified
This derivative version of Qwen3-8B has been adapted for:
- Token usage instrumentation
- Output-length profiling
- Think-block efficiency benchmarking
- Token density and redundancy analysis
- Structured token logging compatibility
- Prompt compression experimentation
- Cost-performance modeling
No architectural claims are made beyond those of the original Qwen3-8B base model. Core weights originate from the Apache-licensed base model.
Purpose of This Fork
RecourseLLM_Q8b is designed for:
- Token efficiency benchmarking
- Enterprise cost optimization modeling
- Prompt engineering research
- Agent workflow token diagnostics
- Reasoning verbosity analysis
- SaaS inference cost reduction experiments
The primary objective is token performance analysis, not altering the fundamental capabilities of the base model.
Base Model Attribution
This model is based on:
Qwen3-8B
- License: Apache 2.0
- Authors: Qwen Team
- Original Repository: https://huggingface.co/Qwen/Qwen3-8B
All original copyrights and license terms remain in effect.
Model Overview (Inherited from Qwen3-8B)
- Type: Causal Language Model
- Parameters: 8 Billion
- Architecture: Dense Transformer
- Training Stage: Pretraining & Post-training
- Context Length: 32,768 tokens
- Supports thinking and non-thinking modes
For full architecture details and benchmark results, please refer to:
Token Optimization Analysis Capabilities
This fork enables structured token analysis across high-capacity reasoning tasks.
1️⃣ Think vs Non-Think Efficiency Benchmarking
Measure:
- Reasoning token overhead
- Think-block token ratio
- Final-answer compression ratio
- Accuracy-per-token delta
- Marginal cost per reasoning step
2️⃣ Token Density Profiling
Analyze:
- Tokens per semantic unit
- Redundancy rates
- Repetition patterns
- Token-per-concept ratio
- Verbosity distribution curves
3️⃣ Enterprise Cost Modeling
Supports:
- Per-request token cost estimation
- Token budget simulation
- Throughput-to-token efficiency modeling
- Multi-agent loop token amplification tracking
- Scaled inference cost projections
4️⃣ Prompt Compression Experiments
Evaluate:
- Minimal prompt vs verbose prompt tradeoffs
- System prompt token footprint
- Multi-turn token growth curves
- Prompt entropy vs output quality
- Instruction density optimization
Intended Use
RecourseLLM_Q8b is intended for:
- Enterprise LLM cost analysis
- Token efficiency research
- Prompt engineering optimization
- Agent orchestration benchmarking
- Controlled inference environments
- Token instrumentation experiments
It is not intended as a production conversational deployment without additional tuning and evaluation.
Licensing
This repository includes Apache 2.0 licensed components from the original Qwen3-8B model.
Your use of this model must comply with the Apache 2.0 License.
Modifications introduced by RecourseLLM are distributed in compliance with Apache 2.0. Additional usage terms may apply depending on deployment context.
Citation
If you find the original Qwen3 work helpful, please cite:
1@misc{qwen3technicalreport,
2 title={Qwen3 Technical Report},
3 author={Qwen Team},
4 year={2025},
5 eprint={2505.09388},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2505.09388}
9}
