shreyansh12183/olmo2-7b-astro-logic

🔭 OLMo-2 7B Astrophysics & Formal Logic Specialist

A dual-domain reasoning engine fine-tuned for relativistic astrophysics, orbital dynamics, and first-order formal deductive logic.

Developed by Shreyansh Singh as part of an exploratory portfolio of domain-specialized neural reasoning engines.

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🔬 Capabilities

• Astrophysical Modeling: Stellar hydrostatic equilibrium, black hole thermodynamics, and cosmological expansion dynamics.
• Formal Logic: First-order predicate calculus proofs, modal logic formulations, and propositional inference validity proofs.

💻 Quick Start & Loading in Python (PEFT LoRA)

This model is uploaded as a PEFT LoRA Adapter. To load and run inference in Python using transformers & peft:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "allenai/OLMo-2-1124-7B-Instruct"
adapter_id = "shreyansh12183/olmo2-7b-astro-logic"

# 1. Load Base Model & Tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# 2. Attach LoRA Adapter
model = PeftModel.from_pretrained(base_model, adapter_id)

# 3. Generate Response
prompt = "Your prompt here..."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🖥️ Running in LM Studio & Ollama (GGUF)

To run this model on laptops/macbooks using LM Studio, Ollama, or Jan.ai:

1. Download the merged GGUF version (available under shreyansh12183/ GGUF repos).
2. For Ollama, run:

   ollama run hf.co/shreyansh12183/olmo2-7b-astro-logic-GGUF:Q4_K_M
   

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Developed by ExperimentLab AI | Official Website: https://experimentlab.in | Contact: shreyansh@experimentlab.in