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README.md

@@ -1,69 +1,127 @@
 ---
 license: apache-2.0
-base_model: Qwen/Qwen2.5-72B-Instruct
-tags:
-  - math
-  - reasoning
-  - qwen2
-  - merged
-  - aimo3
+language:
+  - en
 library_name: transformers
+tags:
+  - geopolitical-analysis
+  - risk-assessment
+  - intelligence
+  - fine-tuned
+  - lora
+  - qwen2.5
+  - fp16
+base_model: Qwen/Qwen2.5-72B-Instruct
+datasets:
+  - custom
 pipeline_tag: text-generation
 model-index:
-  - name: elle-72b-ultimate
+  - name: Elle-72B-Ultimate
     results: []
 ---
 
 # Elle-72B-Ultimate
 
-## Model Description
+**Elle** is a fine-tuned geopolitical intelligence model built on Qwen2.5-72B-Instruct-AWQ, specialized for:
 
-Elle-72B-Ultimate is a fine-tuned version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) optimized for mathematical reasoning and problem-solving, specifically designed for the AI Mathematical Olympiad Progress Prize 3 (AIMO3) competition.
-
-This is a **merged full model** (LoRA adapter merged into base weights).
+- Real-time geopolitical risk assessment
+- Multi-source intelligence synthesis
+- Causal chain analysis for global events
+- Regime stability detection
+- Cascade risk prediction
 
 ## Model Details
 
-- **Base Model**: Qwen/Qwen2.5-72B-Instruct
-- **Parameters**: 72B
-- **Precision**: BF16
-- **Format**: Safetensors (31 shards)
-- **Training Method**: LoRA (r=64, α=128)
+| 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
 
-Fine-tuned on mathematical reasoning datasets including:
-- NuminaMath-CoT
-- Custom mathematical reasoning examples
+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
 
-- Mathematical problem solving
-- Olympiad-style competition problems
-- Code generation for computational solutions
-- Chain-of-thought reasoning
+Elle is designed for:
+
+- Enterprise geopolitical risk dashboards
+- Intelligence briefing generation
+- Supply chain risk assessment
+- Investment risk analysis
+- Policy impact modeling
 
 ## Limitations
 
-- **Size**: ~144GB in BF16 - requires significant VRAM
-- **Quantization Recommended**: For inference on consumer hardware, use AWQ or GPTQ quantized versions
+- 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
+## Usage with vLLM
+
+```python
+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
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model = AutoModelForCausalLM.from_pretrained(
-    "aphoticshaman/elle-72b-ultimate",
-    torch_dtype="auto",
+    "aphoticshaman/Elle-72B-Ultimate",
     device_map="auto",
-    trust_remote_code=True
+    trust_remote_code=True,
 )
-tokenizer = AutoTokenizer.from_pretrained("aphoticshaman/elle-72b-ultimate")
+tokenizer = AutoTokenizer.from_pretrained("aphoticshaman/Elle-72B-Ultimate")
 
 messages = [
-    {"role": "system", "content": "You are an expert mathematical problem solver."},
-    {"role": "user", "content": "Find all positive integers n such that n^2 + 1 divides n^3 + 1."}
+    {"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)
@@ -72,14 +130,50 @@ outputs = model.generate(**inputs, max_new_tokens=2048)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
+## Training Configuration
+
+```yaml
+# 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
 
 ```bibtex
 @misc{elle-72b-ultimate,
-  author = {aphoticshaman},
-  title = {Elle-72B-Ultimate: Mathematical Reasoning Model},
+  author = {LatticeForge},
+  title = {Elle-72B-Ultimate: Fine-tuned Geopolitical Intelligence Model},
   year = {2024},
   publisher = {HuggingFace},
-  url = {https://huggingface.co/aphoticshaman/elle-72b-ultimate}
+  url = {https://huggingface.co/aphoticshaman/Elle-72B-Ultimate}
 }
 ```
+
+## License
+
+Apache 2.0 - See LICENSE file for details.
+
+## Contact
+
+- **Website**: [latticeforge.ai](https://latticeforge.ai)
+- **Issues**: Report issues via HuggingFace discussions

chat_template.jinja

@@ -1,4 +1,54 @@
-{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
-' + message['content'] + '<|im_end|>' + '
-'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
-' }}{% endif %}
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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   "sliding_window": null,
   "tie_word_embeddings": false,
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+  "torch_dtype": "float16",
   "transformers_version": "4.55.2",
   "use_cache": true,
   "use_sliding_window": false,

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-size 11421896
+oid sha256:a8506e7111b80c6d8635951a02eab0f4e1a8e4e5772da83846579e97b16f61bf
+size 7031673

tokenizer_config.json

@@ -195,10 +195,10 @@
     "<|video_pad|>"
   ],
   "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",
-  "extra_special_tokens": {},
   "model_max_length": 131072,
   "pad_token": "<|endoftext|>",
   "split_special_tokens": false,