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Jupiter-G-8B is a post-trained variant of Google Gemma 4 E4B IT, developed by Locai Labs. The G denotes the Gemma base. Jupiter-G-8B improves instruction following (+1.7 IFEval, +1.0 IFBench) and coding/agentic...
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Jupiter
Jupiter-G-8B is a post-trained variant of Google Gemma 4 E4B IT, developed by Locai Labs. 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-NotTM framework. This model was trained on 1 H200 GPU using 100% renewable energy.
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1 fuenteVerificado 15 sept
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1 artefactoExtractos de fuentes
2 extractosWe evaluate Jupiter-G-8B against 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.
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 |
| 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 |
@misc{locailabs2026jupiterg,
title = {Jupiter-G-8B},
author = {George Drayson},
year = {2026},
url = {https://huggingface.co/locailabs/Jupiter-G-8B}
}
Jupiter-G-8B builds on Google Gemma 4. Terminal and programming data are sourced from NVIDIA's Nemotron corpora.
--- 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...
Source context: 74 downloads · 5 likes · Pipeline any-to-any · Library transformers · Repo locailabs/Jupiter-G-8B