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# Qwen3-8B-Elizabeth-Simple
A fine-tuned version of Qwen3-8B specifically optimized for tool use capabilities, trained on the Elizabeth tool use minipack.
## Model Details
### Base Model
- **Model:** Qwen/Qwen3-8B
- **Architecture:** Transformer decoder-only
- **Parameters:** 8 billion
- **Context Length:** 4096 tokens
### Training Details
- **Training Method:** Full fine-tuning (no LoRA/adapters)
- **Precision:** bfloat16
- **Training Data:** Elizabeth tool use minipack (198 high-quality examples)
- **Training Time:** 2 minutes 36 seconds
- **Final Loss:** 0.436 (from 3.27 → 0.16)
- **Hardware:** 2x NVIDIA H200 (283GB total VRAM)
### Performance
- **Training Speed:** 3.8 samples/second
- **Convergence:** Excellent (3.27 → 0.16 loss)
- **Tool Use Accuracy:** Optimized for reliable tool calling
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"LevelUp2x/qwen3-8b-elizabeth-simple",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("LevelUp2x/qwen3-8b-elizabeth-simple")
# Tool use example
prompt = "Please help me calculate the square root of 144 using the calculator tool."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0]))
```
## Training Methodology
### Pure Weight Evolution
This model was trained using pure weight evolution methodology - no external adapters, LoRA, or quantization were used. The entire base model weights were updated to bake Elizabeth's identity and tool use capabilities directly into the model parameters.
### Data Quality
- **Dataset Size:** 198 carefully curated examples
- **Quality:** High-quality tool use demonstrations
- **Diversity:** Multiple tool types and usage patterns
- **Consistency:** Uniform formatting and instruction following
### Optimization
- **Gradient Accumulation:** 16 steps
- **Effective Batch Size:** 64
- **Learning Rate:** 2e-5
- **Optimizer:** AdamW with cosine scheduler
- **Epochs:** 3.0
## Deployment
### Hardware Requirements
- **GPU Memory:** Minimum 80GB VRAM (recommended 120GB+)
- **Precision:** bfloat16 recommended
- **Batch Size:** Optimal batch size of 4
### Serving
Recommended serving with vLLM for optimal performance:
```bash
python -m vllm.entrypoints.api_server \
--model LevelUp2x/qwen3-8b-elizabeth-simple \
--dtype bfloat16 \
--gpu-memory-utilization 0.9
```
## License
Apache 2.0
## Citation
```bibtex
@software{qwen3_8b_elizabeth_simple,
title = {Qwen3-8B-Elizabeth-Simple: Tool Use Fine-Tuned Model},
author = {ADAPT-Chase and Nova Prime},
year = {2025},
url = {https://huggingface.co/LevelUp2x/qwen3-8b-elizabeth-simple},
publisher = {Hugging Face},
version = {1.0.0}
}
```
## Contact
For questions about this model, please open an issue on the Hugging Face repository or contact the maintainers. |