Bmcbob76
/

echo-titlehound-lora

@@ -1,176 +1,113 @@
----
-license: apache-2.0
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-  - lora
-  - title-examination
-  - oil-gas
-  - texas
-  - landman
-  - legal
-  - peft
-  - qwen2.5
-  - echo-omega-prime
-pipeline_tag: text-generation
-library_name: peft
-datasets:
-  - custom
-language:
-  - en
----
-# TitleHound LoRA v1.0 — Texas Oil & Gas Title Examination AI
-Fine-tuned LoRA adapter for **Texas oil and gas title chain analysis**, gap identification, and cure recommendations. Built on Qwen2.5-7B-Instruct with domain-specific training data covering real-world title defects, conveyance gaps, and curative actions across the Permian Basin and beyond.
-## Model Details
-| Parameter | Value |
-|-----------|-------|
-| **Base Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
-| **Method** | QLoRA (4-bit quantization + Low-Rank Adaptation) |
-| **LoRA Rank (r)** | 16 |
-| **LoRA Alpha** | 32 |
-| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
-| **Adapter Size** | 38.5 MB |
-| **Training Examples** | 749 Texas gap-closure scenarios |
-| **Training Framework** | PEFT + Transformers + bitsandbytes |
-| **Precision** | bf16 (adapter weights) / 4-bit (base model during training) |
-## Capabilities
-- **Title Chain Analysis** — Trace ownership from sovereign through current holder, identifying every conveyance, reservation, and exception
-- **Gap Identification** — Detect missing links, wild deeds, after-acquired title issues, unreleased liens, and probate gaps
-- **Cure Recommendations** — Generate specific curative actions (affidavits of heirship, correction deeds, ratification instruments, quiet title suits)
-- **Texas-Specific Knowledge** — Trained on Texas Property Code, Texas Natural Resources Code, and Permian Basin title practices
-- **Mineral/Surface Distinction** — Properly handles severed mineral estates, executive rights, NPRI, and overriding royalty interests
-## Usage
-### vLLM Multi-LoRA (Recommended for Production)
-```python
-from openai import OpenAI
-client = OpenAI(
-    base_url="http://localhost:8000/v1",
-    api_key="token-abc123",
-)
-response = client.chat.completions.create(
-    model="titlehound",
-    messages=[
-        {
-            "role": "system",
-            "content": "You are TitleHound, an expert Texas oil and gas title examiner. Analyze title chains, identify gaps, and recommend curative actions."
-        },
-        {
-            "role": "user",
-            "content": "Analyze this title chain for Section 12, Block A-45, T-1-S, T&P Ry. Co. Survey, Reeves County, TX: 1. State of Texas to J.R. Smith (Patent, 1905). 2. J.R. Smith to First National Bank (Deed of Trust, 1920). 3. First National Bank foreclosure sale to W.H. Jones (1925). 4. W.H. Jones died intestate (1940), no probate filed. 5. Unknown party to Permian Oil Co. (Oil and Gas Lease, 1955). Identify all gaps and recommend cures."
-        }
-    ],
-    temperature=0.3,
-    max_tokens=2048,
-)
-print(response.choices[0].message.content)
-```
-### PEFT + Transformers
-```python
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-base_model_id = "Qwen/Qwen2.5-7B-Instruct"
-adapter_id = "Bmcbob76/echo-titlehound-lora"
-tokenizer = AutoTokenizer.from_pretrained(base_model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-model = PeftModel.from_pretrained(model, adapter_id)
-messages = [
-    {"role": "system", "content": "You are TitleHound, an expert Texas oil and gas title examiner."},
-    {"role": "user", "content": "What curative actions are needed when a grantor in the chain died intestate with no probate filed?"},
-]
-text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-with torch.no_grad():
-    outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.3)
-print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
-```
-### RunPod Serverless
-```bash
-curl -s https://api.runpod.ai/v2/grdoaby5hbon86/openai/v1/chat/completions \
-  -H "Authorization: Bearer YOUR_RUNPOD_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "titlehound",
-    "messages": [
-      {"role": "system", "content": "You are TitleHound, an expert Texas oil and gas title examiner."},
-      {"role": "user", "content": "Analyze this Reeves County title chain for gaps and recommend cures: Patent to Smith (1903), Smith to Jones via warranty deed (1925), Jones estate probated to Mary Jones (1960), Mary Jones to Permian Exploration via OGL (1970), no release of OGL on record, unknown party to Basin Resources via mineral deed (1985)."}
-    ],
-    "temperature": 0.3,
-    "max_tokens": 2048
-  }'
-```
-## Training Details
-**Dataset:** 749 expert-curated examples of Texas oil and gas title gap analysis, each containing:
-- A title chain with one or more defects (missing conveyances, unreleased liens, intestate deaths without probate, wild deeds, conflicting legal descriptions)
-- Expert identification of each gap with legal authority citations
-- Specific curative action recommendations with Texas statutory references
-**Training Configuration:**
-- **Epochs:** 3
-- **Batch Size:** 4 (effective with gradient accumulation)
-- **Learning Rate:** 2e-4 with cosine scheduler
-- **Warmup:** 10% of total steps
-- **Max Sequence Length:** 4096 tokens
-- **Quantization:** 4-bit NormalFloat (NF4) via bitsandbytes
-**Evaluation:** Validated against held-out title scenarios reviewed by licensed Texas title examiners. The model correctly identifies >90% of common gap types and generates legally sound curative recommendations.
-## Limitations
-- Trained primarily on **Texas** title law and Permian Basin practices. Performance on other jurisdictions may vary.
-- Not a substitute for a licensed title examiner or attorney. All outputs should be reviewed by a qualified professional.
-- May not reflect the most recent legislative changes or court decisions after the training cutoff.
-- Complex multi-party mineral estate scenarios with extensive fractional interest calculations may require additional verification.
-## Part of ECHO OMEGA PRIME
-This model is part of the **ECHO OMEGA PRIME** autonomous AI infrastructure — a comprehensive system of 2,600+ specialized AI engines, cloud workers, and knowledge systems built for enterprise-grade domain expertise.
-### Companion Models
-| Model | Description |
-|-------|-------------|
-| [Bmcbob76/echo-doctrine-generator-qlora](https://huggingface.co/Bmcbob76/echo-doctrine-generator-qlora) | Doctrine generation adapter (374K doctrines, 210 domains) |
-| **TitleHound LoRA** (this model) | Texas oil & gas title examination specialist |
-## License
-Apache 2.0 — free for commercial and non-commercial use.
-## Citation
-```bibtex
-@misc{titlehound-lora-2026,
-  title={TitleHound LoRA v1.0: Texas Oil & Gas Title Examination AI},
-  author={Echo Prime Technologies},
-  year={2026},
-  publisher={Hugging Face},
-  url={https://huggingface.co/Bmcbob76/echo-titlehound-lora}
-}
-```

+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - lora
+  - peft
+  - title-examination
+  - oil-gas
+  - texas
+  - landman
+  - legal
+  - mineral-rights
+pipeline_tag: text-generation
+library_name: peft
+---
+# TitleHound LoRA v1.0
+**Texas Oil & Gas Title Examination AI**
+Fine-tuned LoRA adapter for Texas oil and gas title chain analysis, gap identification, and cure recommendations. Trained on 749 real-world Texas title gap-closure examples.
+## Model Details
+| Parameter | Value |
+|-----------|-------|
+| **Base Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
+| **Method** | LoRA (Low-Rank Adaptation) |
+| **Rank (r)** | 16 |
+| **Alpha** | 32 |
+| **Target Modules** | q_proj, k_proj, v_proj, o_proj |
+| **Adapter Size** | 38.5 MB |
+| **Training Examples** | 749 TX gap-closure scenarios |
+| **Domain** | Texas oil & gas title examination |
+## Capabilities
+- **Title Chain Analysis** — Trace ownership chains across complex conveyance histories
+- **Gap Identification** — Detect missing links between recorded instruments
+- **Cure Recommendations** — Suggest specific instruments and actions to cure title defects
+- **Instrument Classification** — Warranty deeds, mineral deeds, assignments, releases, ROW easements
+- **Survey Recognition** — H&GN, T&P, GC&SF, and other Texas railroad surveys
+- **Party Resolution** — Match entity name variations across decades of recordings
+## Usage
+### With vLLM (Multi-LoRA Serving)
+```python
+from openai import OpenAI
+client = OpenAI(
+    base_url="https://api.runpod.ai/v2/grdoaby5hbon86/openai/v1",
+    api_key="YOUR_RUNPOD_API_KEY"
+)
+response = client.chat.completions.create(
+    model="titlehound",  # Selects this LoRA adapter
+    messages=[
+        {"role": "system", "content": "You are TitleHound, a Texas oil and gas title examination AI."},
+        {"role": "user", "content": "Analyze: Gap in chain of title for Section 270, Block 13, H&GN RR Survey, Reeves County TX. Last recorded deed was 1987 warranty deed from Smith to ABC Oil. Current lessee claims through 2005 assignment from XYZ Energy with no recorded link to ABC Oil."}
+    ],
+    max_tokens=500,
+    temperature=0.7
+)
+print(response.choices[0].message.content)
+```
+### With PEFT / Transformers
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
+model = PeftModel.from_pretrained(base_model, "Bmcbob76/echo-titlehound-lora")
+```
+### RunPod Serverless API
+```bash
+curl -X POST "https://api.runpod.ai/v2/grdoaby5hbon86/openai/v1/chat/completions" \
+  -H "Authorization: Bearer $RUNPOD_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "titlehound",
+    "messages": [
+      {"role": "system", "content": "You are TitleHound, a Texas oil and gas title examination AI."},
+      {"role": "user", "content": "What should I look for when there is a gap between a 1987 warranty deed and a 2005 assignment with no recorded link?"}
+    ],
+    "max_tokens": 300
+  }'
+```
+## Training Details
+- **Base**: Qwen/Qwen2.5-7B-Instruct (7 billion parameters)
+- **Data**: 749 Texas title gap-closure examples covering Reeves, Loving, Ward, Pecos, and other Permian Basin counties
+- **Scenarios**: Missing conveyances, entity name mismatches, unrecorded assignments, fractional interest discrepancies, survey description errors, heir property gaps
+- **Format**: System prompt + user query + expert title examiner response
+## Companion Model
+This adapter is deployed alongside [echo-doctrine-generator-qlora](https://huggingface.co/Bmcbob76/echo-doctrine-generator-qlora) (QLoRA, r=64, 632MB) on the same vLLM endpoint for multi-LoRA serving.
+## Infrastructure
+Part of the **ECHO OMEGA PRIME** AI platform — a comprehensive autonomous AI infrastructure with 2,600+ knowledge engines, 31+ Cloudflare Workers, and custom-trained models for specialized domains.
+## License
+Apache 2.0