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

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:

python -m vllm.entrypoints.api_server \
  --model LevelUp2x/qwen3-8b-elizabeth-simple \
  --dtype bfloat16 \
  --gpu-memory-utilization 0.9

License

Apache 2.0

Citation

@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.