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apollo-astralis-8b

Text Generation
PEFT
Safetensors
GGUF
English
qwen3
reasoning
collaboration
friendly
personality
conversational
qwen
lora
vanta-research
apollo
astralis
large-language-model
research
open-source
VANTA
warm
cognitive
logic
LLM
qwen3-finetune
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apollo-astralis-8b / MODEL_CARD.md
Tyler Williams
release: Apollo Astralis 8B (adapters + Q4_K_M GGUF)
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Model Card: Apollo Astralis 8B

Summary

  • 8B reasoning model with a warm, collaborative assistant style; built on Qwen/Qwen3-8B via LoRA.
  • Demonstrates +36% overall improvement vs Base Qwen 8B in a lightweight standard suite (manual-verified): 93% (13/14).
  • VRRE semantic evaluation: 22% automated (answer extraction issue) and 89% manual-verified.
  • Conservative fine-tuning (292 examples) preserves reasoning quality; loss reduced 0.91 → 0.39.
  • Distributed as PEFT adapters; GGUF Q4_K_M also available for local/Ollama.

Key Specifications

  • Name: Apollo Astralis 8B
  • Base Model: Qwen/Qwen3-8B
  • Type: Causal LM with LoRA adapters (rank 16, alpha 32, ~67M trainable params)
  • Context Window: 40K (inherited from base)
  • License: Apache 2.0
  • Release: October 2025

Intended Use

Appropriate

  • Reasoning-intensive tasks with step-by-step explanations (math, logic, structured analysis)
  • Educational assistance, research support, code reasoning, tutoring

Out of Scope

  • Professional legal/medical/financial advice or high-stakes decisions without human oversight
  • Contexts requiring strictly formal/neutral tone throughout

Training Overview

  • Methodology: Conservative LoRA on a proven reasoning baseline (V3), layering personality without degrading capability.
  • Data: 292 curated examples spanning mathematical/logical reasoning and collaborative tone.
  • Hardware/Runtime: 1× RTX 3060 (12GB), ~2 hours, bfloat16.
  • Outcome: Stable convergence (0.91 → 0.39) and preserved reasoning behavior.

Evaluation Highlights

Lightweight benchmark suite (manual-verified; compared to Base Qwen 8B):

Benchmark Base Qwen3 8B Apollo Δ
MMLU (5) 40% (2/5) 100% (5/5) +60%
GSM8K (4) 75% (3/4) 100% (4/4) +25%
HellaSwag (2) 50% (1/2) 50% (1/2) 0%
ARC (3) 67% (2/3) 100% (3/3) +33%
Overall (14) 57% (8/14) 93% (13/14) +36%

VRRE (VANTA Research Reasoning Evaluation)

  • Automated: 22% (2/9) due to answer extraction from blocks rather than final conclusions.
  • Manual-verified: 89% (8/9) with clear, step-by-step reasoning and consistent tone.

Note: Models that surface chain-of-thought often require tailored extraction to evaluate accurately.

Artifacts and Deployment

  • Adapters: PEFT (adapter_model.safetensors, adapter_config.json)
  • Quantization: GGUF Q4_K_M (~4.7GB) for local and Ollama deployment
  • Usage and Modelfiles: see README.md (quick starts, conservative/unlimited variants)
  • Programmatic and integrations: see USAGE_GUIDE.md (Transformers + PEFT, FastAPI/Gradio)
  • Merging and conversion: see MERGE_GUIDE.md (merge adapters, convert to GGUF)

Limitations

  • Explanatory style can be verbose; downstream systems should extract conclusions post-.
  • Optimized for English reasoning; not tuned for highly specialized domains or creative writing.
  • Educational intent; verification recommended for critical use cases; maintain human oversight.

Citation 

@misc{apollo-astralis-8b-2025,
  title={Apollo Astralis 8B: Conservative LoRA Fine-tuning for Reasoning and Personality},
  author={VANTA Research},
  year={2025},
  url={https://huggingface.co/vanta-research/apollo-astralis-8b},
  note={Base: Qwen/Qwen3-8B}
}

Contact

  • [email protected]
  • Hugging Face: vanta-research/apollo-astralis-8b
  • GitHub: vanta-research/apollo-astralis-8b