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README.md

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----
-base_model: unsloth/gemma-2-2b
-library_name: peft
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-## Uses
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-### Direct Use
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-#### Training Hyperparameters
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-## Evaluation
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Framework versions
-
-- PEFT 0.12.0
+# Content Classification LoRA Adapter for Gemma-2B
+
+A LoRA adapter for unsloth/gemma-2b that determines content indexing suitability using chain-of-thought reasoning.
+
+## Technical Specifications
+
+### Base Model
+- Model: unsloth/gemma-2b
+- LoRA Rank: 64
+- Target Modules: q_proj, up_proj, down_proj, gate_proj, o_proj, k_proj, v_proj
+- Task: CAUSAL_LM
+- Dropout: 0
+- Alpha: 32
+
+### Input/Output Format
+
+Input XML structure:
+```xml
+<instruction>Determine true or false if the following content is suitable and should be indexed.</instruction>
+<suitable>
+  <content>{input_text}</content>
+</suitable>
+```
+
+Output XML structure:
+```xml
+<thinking>{reasoning_process}</thinking>
+<category>{content_type}</category>
+<should_index>{true|false}</should_index>
+```
+
+## Deployment
+
+### VLLM Server Setup
+```bash
+export VLLM_ALLOW_RUNTIME_LORA_UPDATING=1
+export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+
+vllm serve unsloth/gemma-2-2b \
+  --gpu-memory-utilization=1 \
+  --port 6002 \
+  --served-model-name="gemma" \
+  --trust-remote-code \
+  --max-model-len 8192 \
+  --disable-log-requests \
+  --enable-lora \
+  --lora-modules lora=./dataset/output/unsloth/lora_model \
+  --max-lora-rank 64
+```
+
+### Processing Pipeline
+
+1. Install Dependencies:
+```bash
+pip install requests tqdm concurrent.futures
+```
+
+2. Run Content Processor:
+```bash
+python process.py --input corpus.jsonl --output results.jsonl --threads 24
+```
+
+### Client Implementation
+
+```python
+import requests
+
+def classify_content(text: str, vllm_url: str = "http://localhost:6002/v1/completions") -> dict:
+    xml_content = (
+        '<instruction>Determine true or false if the following content is '
+        'suitable and should be indexed.</instruction>\n'
+        '<suitable>\n'
+        f'  <content>{text}</content>'
+    )
+    
+    response = requests.post(
+        vllm_url,
+        json={
+            "prompt": xml_content,
+            "max_tokens": 6000,
+            "temperature": 1,
+            "model": "lora",
+            "stop": ["</suitable>"]
+        },
+        timeout=30000
+    )
+    
+    completion = response.json()["choices"][0]["text"]
+    
+    # Parse XML tags
+    import re
+    def extract_tag(tag: str) -> str:
+        match = re.search(f'<{tag}>(.*?)</{tag}>', completion, re.DOTALL)
+        return match.group(1).strip() if match else ""
+        
+    return {
+        "thinking": extract_tag("thinking"),
+        "category": extract_tag("category"),
+        "should_index": extract_tag("should_index")
+    }
+```
+
+### Example Usage
+
+```python
+text = """Multiservice Tactics, Techniques, and Procedures
+for
+Nuclear, Biological, and Chemical Aspects of Consequence
+Management
+
+TABLE OF CONTENTS..."""
+
+result = classify_content(text)
+print(result)
+```
+
+Example output:
+```json
+{
+    "thinking": "This is a table of contents for a document, not the actual content.",
+    "category": "table of contents",
+    "should_index": "false"
+}
+```
+
+## Batch Processing
+
+The included processor supports parallel processing of JSONL files:
+
+```python
+from request_processor import RequestProcessor
+
+processor = RequestProcessor(
+    input_file="corpus.jsonl",
+    output_file="results.jsonl",
+    num_threads=24
+)
+processor.process_file()
+```
+
+Input JSONL format:
+```json
+{
+    "pid": "document_id",
+    "docid": "path/to/source",
+    "content": "document text",
+    "metadata": {
+        "key": "value"
+    }
+}
+```
+
+Output JSONL format:
+```json
+{
+    "pid": "document_id",
+    "docid": "path/to/source",
+    "content": "document text",
+    "metadata": {
+        "key": "value"
+    },
+    "thinking": "reasoning process",
+    "category": "content type",
+    "should_index": "true/false",
+    "processed_at": "2024-10-22 02:52:33"
+}
+```
+
+## Performance Considerations
+
+- Uses thread pooling for parallel processing
+- Implements atomic writes with file locking
+- Progress tracking with tqdm
+- Automatic error handling and logging
+- Configurable thread count for optimization
+
+## Error Handling
+
+Errors are captured in the output JSONL:
+```json
+{
+    "error": "error message",
+    "processed_at": "timestamp"
+}
+```
+
+Monitor errors in real-time:
+```bash
+tail -f results.jsonl | grep error
+```