metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- tool-use
- fine-tuned
- qwen3
- 8b
- elizabeth
pipeline_tag: text-generation
Model Card for Qwen3-8B-Elizabeth-Simple
Model Details
Model Description
- Developed by: ADAPT-Chase
- Model type: Transformer-based language model
- Language(s): English
- License: Apache 2.0
- Finetuned from: Qwen/Qwen3-8B
Model Sources
- Repository: https://huggingface.co/LevelUp2x/qwen3-8b-elizabeth-simple
- Paper: N/A
- Demo: N/A
Uses
Direct Use
This model is designed for tool use and function calling tasks. It can be used for:
- Automated tool invocation
- API calling
- Function execution
- Task automation
- Agent systems
Out-of-Scope Use
- Medical advice
- Legal decisions
- Financial recommendations
- Harmful content generation
Bias, Risks, and Limitations
This model inherits biases from its base model Qwen3-8B and may exhibit:
- Social biases present in training data
- Limitations in tool use accuracy
- Potential hallucination of tool responses
Recommendations
Users should:
- Validate tool outputs
- Implement safety checks
- Monitor for unexpected behavior
- Use in controlled environments
Training Details
Training Data
- Dataset: Elizabeth tool use minipack
- Samples: 198 high-quality examples
- Format: Instruction-response pairs with tool calls
Training Procedure
- Training regime: Full fine-tuning
- Precision: bfloat16
- Hardware: 2x NVIDIA H200
- Training time: 2 minutes 36 seconds
Training Hyperparameters
- Learning rate: 2e-5
- Batch size: 4 (effective 64 with accumulation)
- Epochs: 3.0
- Optimizer: AdamW
- Scheduler: Cosine
Evaluation
Testing Data
- Factors: Tool use accuracy, response quality
- Metrics: Loss, perplexity, tool call success rate
Results
- Final loss: 0.436
- Training speed: 3.8 samples/second
- Convergence: Excellent (3.27 → 0.16)
Environmental Impact
- Hardware Type: NVIDIA H200 GPUs
- Hours used: 0.043 hours
- Cloud Provider: Private infrastructure
- Carbon Emitted: Minimal (estimated < 0.1 kgCO2eq)
Technical Specifications
Model Architecture and Objective
- Architecture: Transformer decoder
- Objective: Causal language modeling
- Params: 8 billion
- Context length: 4096
Compute Infrastructure
- Hardware: 2x NVIDIA H200
- VRAM used: ~120GB during training
Citation
BibTeX:
@software{qwen3_8b_elizabeth_simple_2025,
title = {Qwen3-8B-Elizabeth-Simple},
author = {ADAPT-Chase and Nova Prime},
year = {2025},
url = {https://huggingface.co/LevelUp2x/qwen3-8b-elizabeth-simple},
publisher = {Hugging Face}
}
Glossary
- Pure Weight Evolution: Full fine-tuning without adapters
- Tool Use: Ability to call external functions/APIs
- bfloat16: Brain floating point format
Model Card Authors
ADAPT-Chase and Nova Prime
How to Get Help
Open an issue on the Hugging Face repository or contact the maintainers.