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README.md

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----
-license: apache-2.0
----
+
+# Model Card for nexa-OLMo-sci7b
+
+## Model Details
+**Model Description:**  
+nexa-OLMo-sci7b is a fine-tuned variant of allenai/OLMo-7B, optimized for scientific research generation tasks such as hypothesis generation, abstract writing, and methodology completion. Fine-tuning was performed using PEFT with LoRA in 4-bit quantized mode via bitsandbytes.
+
+**Developed by:** Allan (Independent Scientific Intelligence Architect)  
+**Shared by:** Allan (https://huggingface.co/allan-wandia)  
+**Model type:** Decoder-only transformer (causal language model)  
+**Language(s):** English (scientific domain-specific vocabulary)  
+**License:** Apache 2.0  
+**Fine-tuned from:** allenai/OLMo-7B  
+**Repository:** https://huggingface.co/allan-wandia/nexa-olmo-sci7b  
+
+## Training Details
+**Training Data:**  
+- Size: 100 million tokens  
+- Source: Curated scientific literature (Bio, Physics, QST, Astro)  
+
+**Hyperparameters:**  
+- Sequence length: 1024  
+- Batch size: 1  
+- Gradient Accumulation Steps: 64  
+- Effective Batch Size: 64  
+- Learning rate: 2e-05  
+- Epochs: 2  
+- LoRA: Enabled (PEFT)  
+- Quantization: 4-bit  
+
+**Results:**  
+Robust performance in scientific prose tasks, with novelty varying by prompt diversity.

adapter_config.json

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+  "init_lora_weights": true,
+  "layer_replication": null,
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+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
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+  "target_modules": [
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+    "attn_out",
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+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": true
+}

adapter_model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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+}

training_args.bin

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