Jupiter G 8B GGUF

--- license: apache-2.0 library_name: transformers pipeline_tag: any-to-any base_model: - google/gemma-4-E4B-it tags: - locai - jupiter - gemma - post-training ---  # Jupiter-G-8B Jupiter-G-8B is a post-trained variant of [Google Gemma 4 E4B IT](https://huggingface.co/google/gemma-4-E4B-it), developed by [Locai Labs](https://locailabs.com/). The G denotes the Gemma base. Jupiter-G-8B improves instruction following (+1.7 IFEval, +1.0 IFBench) and coding/agentic capability (+3.2 LCB pass@1) while preserving the base model's reasoning and knowledge through our Forget-Me-Not™ framework. This model was trained on 1 H200 GPU using 100% renewable energy. ## Benchmarks We evaluate Jupiter-G-8B against [gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it). | Benchmark | Jupiter-G-8B | gemma-4-E4B-it | | --- | --- | --- | | IFEval (prompt strict) | **89.3** | 87.6 | | IFBench (prompt strict) | **35.4** | 34.4 | | AgentHarm harm rate | **12.0** | 22.3 | | MMLU Redux | 82.0 | **83.4** | | LiveCodeBench v6 | **55.2** | 52.0 | IFEval and IFBench both reported with prompt strict accuracy. LiveCodeBench v6 reported with pass@1. ## Training ### Post-Training Data Jupiter-G-8B is fine-tuned on a curated mixture of five datasets: | Dataset | Domain | N | | --- | --- | --- | | Self-cognition (non-reasoning) | Identity | ~full | | UltraChat (reasoning + non-reasoning) | Reasoning / Replay | 12,500 | | Nemotron terminal trajectories (reasoning) | Terminal / Agentic | 20,000 | | Nemotron competitive programming (non-reasoning) | Coding | 20,000 | ### Training Configuration | Method | LoRA (rank 16, alpha 32) | | --- | --- | | Target Modules | All linear layers | | Epochs | 2 | | Optimiser | AdamW (fused) | | Learning rate | 2e-4 (cosine decay, 5% warmup) | | Weight decay | 0.001 | | Max grad norm | 1.0 | | Batch size | 64 (global: 8 local x 8 accumulation) | | Sequence length | 2,048 | | Precision | BF16 | | Gradient checkpointing | Enabled | | Loss | Assistant-only | | Kernel | Liger | | Attention | Eager | ### Key Techniques - **Forget-Me-Not:** Synthetic replay data generated by the unmodified base model on UltraChat prompts, preserving existing capabilities during domain-specific fine-tuning. - **Agentic/terminal training:** Curated terminal trajectories from NVIDIA's Nemotron-Terminal-Corpus. - **Competitive programming:** Exe...