Upload gemma2-mql-v2 Q4_K_M GGUF + training data

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+gemma2-mql-v2-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

Modelfile

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+FROM /Users/pg47711/Downloads/content/gemma2-mql-v2-Q4_K_M.gguf
+
+TEMPLATE """{{ if .System }}<start_of_turn>system
+{{ .System }}<end_of_turn>
+{{ end }}{{ if .Prompt }}<start_of_turn>user
+{{ .Prompt }}<end_of_turn>
+{{ end }}<start_of_turn>model
+{{ .Response }}<end_of_turn>
+"""
+
+SYSTEM """You are a MongoDB query generator. Given a natural language question about files in the EntityViews collection, generate the correct MongoDB query (MQL) as JSON. Always use $regex with case-insensitive matching for name searches unless an exact path is provided. The collection has fields: name, extension, format, size, type, entity_type, has_pii, processed, create_time, modified_time, access_time, permissions, content_attributes, change_type, errors, custom_attributes."""
+
+PARAMETER temperature 0.1
+PARAMETER top_p 0.9
+PARAMETER stop "<end_of_turn>"

README.md

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+---
+license: apache-2.0
+base_model: google/gemma-2-2b-it
+tags:
+  - text-to-mql
+  - mongodb
+  - netapp
+  - aidp
+  - gemma2
+  - gguf
+  - fine-tuned
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Gemma 2 2B - Text to MQL (MongoDB Query Language)
+
+A fine-tuned Gemma 2 2B-IT model that converts natural language questions into MongoDB queries (MQL) for the AIDP EntityViews collection.
+
+## Model Details
+
+- **Base model**: google/gemma-2-2b-it
+- **Fine-tuned on**: 1,164 text-to-MQL training samples
+- **Task**: Natural language to MongoDB query generation
+- **Format**: GGUF (Q4_K_M quantized)
+- **Size**: 1.6 GB
+
+## Available Files
+
+| File | Format | Size | Description |
+|---|---|---|---|
+| `gemma2-mql-v2-Q4_K_M.gguf` | GGUF Q4_K_M | 1.6 GB | Recommended - best size/quality balance |
+| `gemma2-mql-v2-f16.gguf` | GGUF F16 | 4.9 GB | Full precision |
+
+## Supported Fields
+
+The model generates MQL queries for the EntityViews collection with these fields:
+- `name` - file path/name
+- `extension` - file extension (txt, tiff, exr, usd, jpg, png)
+- `format` - file format
+- `size` - file size in bytes
+- `type` / `entity_type` - entity type
+- `has_pii` - whether file contains PII
+- `processed` - processing status
+- `create_time` / `modified_time` / `access_time` - timestamps
+- `permissions` - Unix permissions (e.g., rw-r--r--)
+- `content_attributes` - extracted content metadata including image descriptions
+- `change_type` - ADDED, MODIFIED, etc.
+- `errors` - processing errors
+- `custom_attributes` - custom metadata
+
+## Usage with Ollama
+
+```bash
+# Download the GGUF file, then create a Modelfile:
+cat > Modelfile << 'EOF'
+FROM ./gemma2-mql-v2-Q4_K_M.gguf
+
+TEMPLATE """{{ if .System }}<start_of_turn>system
+{{ .System }}<end_of_turn>
+{{ end }}{{ if .Prompt }}<start_of_turn>user
+{{ .Prompt }}<end_of_turn>
+{{ end }}<start_of_turn>model
+{{ .Response }}<end_of_turn>
+"""
+
+SYSTEM """You are a MongoDB query generator. Given a natural language question about files in the EntityViews collection, generate the correct MongoDB query (MQL) as JSON. Always use $regex with case-insensitive matching for name searches unless an exact path is provided."""
+
+PARAMETER temperature 0.1
+PARAMETER top_p 0.9
+PARAMETER stop "<end_of_turn>"
+EOF
+
+ollama create gemma2-mql -f Modelfile
+ollama run gemma2-mql "Find all tiff files larger than 10MB"
+```
+
+## Usage with vLLM (NVIDIA L4 GPU)
+
+```bash
+# For the F16 safetensors version, use the base fine-tuned model directly:
+pip install vllm
+
+python -m vllm.entrypoints.openai.api_server \
+  --model <your-hf-username>/gemma2-mql-v2 \
+  --dtype bfloat16 \
+  --max-model-len 4096 \
+  --gpu-memory-utilization 0.9
+```
+
+## Example Queries
+
+| Input | Generated MQL |
+|---|---|
+| "Find all tiff files larger than 10MB" | `{"$and": [{"extension": "tiff"}, {"size": {"$gt": 10485760}}]}` |
+| "Show files with PII" | `{"has_pii": true}` |
+| "Find the file water-droplet" | `{"name": {"$regex": ".*water-droplet.*", "$options": "i"}}` |
+| "How many exr files are there" | `{"collection": "EntityViews", "method": "count", "filter": {"extension": "exr"}}` |
+| "Show me top 5 files by sizes" | `{"collection": "EntityViews", "method": "find", "filter": {}, "sort": {"size": -1}, "limit": 5}` |
+| "What images show the Golden Gate Bridge" | `{"content_attributes": {"$elemMatch": {"key": "Image Description", "value": {"$regex": ".*Golden Gate Bridge.*", "$options": "i"}}}}` |
+
+## Training Data
+
+Fine-tuned on 1,164 samples covering:
+- Name search (exact, regex, path-based): 437 samples
+- Extension filters: 267 samples
+- Size comparisons: 177 samples
+- Content attribute search: 176 samples
+- Compound queries: 112 samples
+- PII/processed flags: 116 samples
+- Permissions (Unix notation): 59 samples
+- Count/distinct/sort operations: 116 samples
+- Error detection: 26 samples

gemma2-mql-v2-Q4_K_M.gguf

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