platform/aiml/experiments/model_card.md

---
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.