Elle-72B-Ultimate

Elle is a fine-tuned geopolitical intelligence model built on Qwen2.5-72B-Instruct-AWQ, specialized for:

• Real-time geopolitical risk assessment
• Multi-source intelligence synthesis
• Causal chain analysis for global events
• Regime stability detection
• Cascade risk prediction

Model Details

Attribute	Value
Base Model	Qwen/Qwen2.5-72B-Instruct
Fine-tuning Method	LoRA (r=64, alpha=128)
Training Framework	Unsloth + PEFT
Precision	FP16 (full precision merged)
Context Length	32,768 tokens
Final Training Loss	0.2544

Training Data

Elle was trained on curated geopolitical intelligence data including:

• GDELT Event Data: Global event monitoring and conflict detection
• World Bank Indicators: Economic stability metrics
• USGS Seismic Data: Natural disaster risk factors
• Curated Intel Briefings: Expert-verified geopolitical analysis
• Cascade Analysis: Historical event chain patterns

Training used interleaved conversation format with system prompts, user queries, and assistant responses.

Intended Use

Elle is designed for:

• Enterprise geopolitical risk dashboards
• Intelligence briefing generation
• Supply chain risk assessment
• Investment risk analysis
• Policy impact modeling

Limitations

• Knowledge cutoff aligned with training data (Dec 2024)
• Requires external data feeds for real-time analysis
• Should be used as analytical support, not sole decision-maker
• May reflect biases present in training data sources

Hardware Requirements

• Inference: 4x H100/H200 80GB (vLLM recommended)
• Memory: ~280GB VRAM for FP16 model (4x H200 = 320GB)
• Consider quantizing to AWQ/GPTQ for smaller deployments

Usage with vLLM

from vllm import LLM, SamplingParams

llm = LLM(
    model="aphoticshaman/Elle-72B-Ultimate",
    tensor_parallel_size=4,
    trust_remote_code=True,
    max_model_len=32768,
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=4096,
)

prompt = """<|im_start|>system
You are Elle, an expert geopolitical intelligence analyst.
<|im_end|>
<|im_start|>user
Analyze the current risk factors affecting semiconductor supply chains.
<|im_end|>
<|im_start|>assistant
"""

outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)

Usage with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "aphoticshaman/Elle-72B-Ultimate",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("aphoticshaman/Elle-72B-Ultimate")

messages = [
    {"role": "system", "content": "You are Elle, an expert geopolitical intelligence analyst."},
    {"role": "user", "content": "What are the key risk indicators for the South China Sea region?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Configuration

# LoRA Configuration
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj

# Training Hyperparameters
learning_rate: 2e-5
batch_size: 2
gradient_accumulation_steps: 8
epochs: 3
warmup_ratio: 0.03
lr_scheduler: cosine
optimizer: adamw_8bit
max_seq_length: 8192

Citation

@misc{elle-72b-ultimate,
  author = {LatticeForge},
  title = {Elle-72B-Ultimate: Fine-tuned Geopolitical Intelligence Model},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/aphoticshaman/Elle-72B-Ultimate}
}

License

Apache 2.0 - See LICENSE file for details.

Contact

• Website: latticeforge.ai
• Issues: Report issues via HuggingFace discussions