Instructions to use ravinarayanan/gemma-2b-soql-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ravinarayanan/gemma-2b-soql-qlora with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ravinarayanan/gemma-2b-soql-qlora",
	filename="gemma-4-e2b-it.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ravinarayanan/gemma-2b-soql-qlora with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Use Docker

docker model run hf.co/ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

LM Studio
Jan

vLLM

How to use ravinarayanan/gemma-2b-soql-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ravinarayanan/gemma-2b-soql-qlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ravinarayanan/gemma-2b-soql-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Ollama
How to use ravinarayanan/gemma-2b-soql-qlora with Ollama:
```
ollama run hf.co/ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
```

Unsloth Studio

How to use ravinarayanan/gemma-2b-soql-qlora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ravinarayanan/gemma-2b-soql-qlora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ravinarayanan/gemma-2b-soql-qlora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ravinarayanan/gemma-2b-soql-qlora to start chatting

How to use ravinarayanan/gemma-2b-soql-qlora with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ravinarayanan/gemma-2b-soql-qlora:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ravinarayanan/gemma-2b-soql-qlora with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ravinarayanan/gemma-2b-soql-qlora with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ravinarayanan/gemma-2b-soql-qlora:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ravinarayanan/gemma-2b-soql-qlora with Docker Model Runner:
```
docker model run hf.co/ravinarayanan/gemma-2b-soql-qlora:Q4_K_M
```

Lemonade

How to use ravinarayanan/gemma-2b-soql-qlora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ravinarayanan/gemma-2b-soql-qlora:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2b-soql-qlora-Q4_K_M

List all available models

lemonade list

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Gemma 4 E2B — SOQL Query Generator (QLoRA, Q4_K_M GGUF)

Fine-tuned from google/gemma-4-2b-it to translate natural-language questions into SOQL (Salesforce Object Query Language) queries.

Note: The model is trained and prompted to return bare SOQL, but like all small LLMs it may occasionally include reasoning traces (<think>...</think> blocks), brief explanations, or off-script text. Always validate the output and strip non-SOQL content before execution. The reference orchestrator (orchestrator/app.py) handles this automatically.

Model details

Property	Value
Base model	`unsloth/gemma-4-E2B-it` (4-bit, Unsloth day-0 quant)
Fine-tune method	QLoRA — LoRA r=16, alpha=16
LoRA target modules	q/k/v/o_proj + gate/up/down_proj
Training hardware	A100 GPU
Training steps	250 (early stop; val loss 0.119)
Dataset	11,040 synthetic NL→SOQL pairs, 17 SOQL pattern types
Quantization	Q4_K_M GGUF (~1.5 GB)
Context window	2048 tokens

What it does

Given a Salesforce object schema + a natural-language question, the model returns a SOQL SELECT query. It was trained on fictional object/field names (Vehicle__c, Driver__c, etc.) to learn SOQL syntax — not any specific org's schema. Inject your real org's schema in the user message at inference time.

SOQL patterns covered

Simple SELECT · WHERE filters · ORDER BY · LIMIT · COUNT/aggregate · GROUP BY · HAVING · date literals · multi-field · relationship queries · subquery IN filters · parent field traversal · child subqueries · LIKE · NULL checks · boolean logic · semi-joins

Usage with Ollama (recommended)

# Pull directly from this repo
ollama pull hf.co/ravinarayanan/gemma-2b-soql-qlora

# Or create from the bundled Modelfile
ollama create soql-gemma4 -f Modelfile
ollama run soql-gemma4

Example prompt

Object: Vehicle__c
Fields: Name, Make__c, Model__c, Year__c, Price__c, FuelType__c, IsAvailable__c
Relationships: Dealership__c (lookup), ServiceRecords__r (child)

Write a SOQL query to find electric vehicles priced under 25000 that are available

Example output

The model typically returns bare SOQL, but may prefix it with a <think> block. Strip everything before and including </think> if present:

SELECT Name, Make__c, Model__c, Price__c
FROM Vehicle__c
WHERE FuelType__c = 'Electric'
AND Price__c < 25000
AND IsAvailable__c = true

Parsing the output in Python

import re

def extract_soql(raw: str) -> str:
    # Strip <think>...</think> block if present
    raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
    # Take only the first SELECT statement
    match = re.search(r"(SELECT\b.*)", raw, re.IGNORECASE | re.DOTALL)
    return match.group(1).strip() if match else raw.strip()

System prompt used at inference

You are a SOQL query generator. Given a Salesforce schema and a question,
output ONLY the bare SOQL query. No explanation, no markdown, no extra text,
no reasoning.

Limitations

Output not guaranteed to be pure SOQL — the model may include reasoning traces or brief text; validate and strip before execution
Trained on synthetic data — always test generated queries against your org
Only SELECT queries — DML (INSERT/UPDATE/DELETE) is intentionally excluded from training
Schema must be provided in the user message; the model has no live Salesforce access
2B parameter model — complex nested subqueries may need a retry or prompt adjustment

Training data

11,040 synthetic training examples generated from a fictional Salesforce schema (4 objects: Vehicle__c, Driver__c, Dealership__c, ServiceRecord__c) covering 17 SOQL pattern types. Dataset was generated with a capability-aware template generator to ensure pattern diversity and avoid duplicates.

License

This model is derived from Gemma 4 and is subject to the Gemma Terms of Use.

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GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

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