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---
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:**
```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. |