Instructions to use aphoticshaman/elle-72b-ultimate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aphoticshaman/elle-72b-ultimate with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aphoticshaman/elle-72b-ultimate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aphoticshaman/elle-72b-ultimate")
model = AutoModelForCausalLM.from_pretrained("aphoticshaman/elle-72b-ultimate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aphoticshaman/elle-72b-ultimate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aphoticshaman/elle-72b-ultimate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aphoticshaman/elle-72b-ultimate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aphoticshaman/elle-72b-ultimate

SGLang

How to use aphoticshaman/elle-72b-ultimate with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aphoticshaman/elle-72b-ultimate" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aphoticshaman/elle-72b-ultimate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aphoticshaman/elle-72b-ultimate" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aphoticshaman/elle-72b-ultimate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aphoticshaman/elle-72b-ultimate with Docker Model Runner:
```
docker model run hf.co/aphoticshaman/elle-72b-ultimate
```

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}
}