relm-1

relm-1 is a specialized post-train of Gemma 4 (31B-it) designed to excel in complex reasoning, high-difficulty SQL generation, telco software, and specialized coding tasks.

Model Summary

• Base Model: google/gemma-4-31B-it
• Training Method: Post-trained via LoRA (Low-Rank Adaptation) using the scalarlm framework, with weights merged back into the base model for standalone deployment.
• Specializations: Complex SQL, OpenTelco (OTel), Relational AI PyRel, and General Reasoning.
• Context Window (SFT): 6,144 tokens

🎯 Intended Use

relm-1 is designed for developers and data engineers who need a model capable of:

• Advanced SQL Generation: Solving high-complexity database queries, including specialized Snowflake SQL tasks and benchmarks based on Spider-2.
• Relational AI PyRel: Generating and reasoning over PyRel, a Pythonic relational language that translates high-level logic into optimized SQL.
• Observability Analysis: Understanding and generating telco prompts and completions (OTel-LLM).
• Reasoning-Heavy Tasks: Leveraging Chain-of-Thought (CoT) to decompose complex software engineering problems.

🛠️ Training Details

Dataset Composition

The model underwent post-training on a balanced mixture of high-quality SFT data:

Dataset	Weight	Focus
Relational AI & PyRel	50%	PyRel and relational logic
Spider-2	20%	Complex SQL generation
Nemotron-Cascade-2-SFT	10%	General chat and instruction following
OTel-LLM	10%	OpenTelco
Specialized Snowflake SQL	10%	Advanced Snowflake SQL tasks

Training Hyperparameters

• Learning Rate: 2e-4
• Max Steps: 6,000
• Gradient Accumulation: 2
• Architecture: LoRA adaptation merged into the 31B base model.

✍️ Prompting & Usage

Reasoning Format

relm-1 utilizes Gemma 4's native channel-thought format for reasoning. To trigger Chain-of-Thought, the model is trained to follow this structure:

<thought>
{Step-by-step reasoning and analysis}
</thought>
{Final answer}

Specialized Directives

The model responds to specific formatting directives injected during post-training:
- FINOS Legend: For tasks requiring the "Pure" version of FINOS Legend, the model is optimized for the prompt: "Answer using FINOS Legend (Pure language)."
- Hybrid SQL/PyRel: For tasks requiring dual implementations, the model is optimized for: "Provide both a SQL solution and a PyRel solution."

Chat Template

The model follows the official Gemma 4 template:

<|user|>
{prompt}<|end_of_turn|>
<|model|>
{response}<|end_of_turn|>

⚠️  Limitations

- Context Window: Post-training samples were filtered to a maximum of 6,144 tokens; performance on extremely long documents may vary.
- Base Model Dependency: While the weights are merged, the model's foundational capabilities are inherited from the Gemma 4 31B-it base.