Create README.md

README.md

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+---
+license: mit
+datasets:
+- starmpcc/Asclepius-Synthetic-Clinical-Notes
+language:
+- en
+---
+## Overview
+
+This model, elucidator8918/clinical-ehr-prototype-0.2_GGUF is a Q4_K_M type GGUF and is tailored for clinical documentation, based on the Mistral-7B-Instruct-v0.3-sharded architecture fine-tuned on the Asclepius-Synthetic-Clinical-Notes dataset.
+
+## Key Information
+
+- **Model Name**: Mistral-7B-Instruct-v0.3-sharded
+- **Fine-tuned Model Name**: elucidator8918/clinical-ehr-prototype-0.2_GGUF
+- **Dataset**: starmpcc/Asclepius-Synthetic-Clinical-Notes
+- **Language**: English (en)
+
+## Model Details
+
+- **LoRA Parameters (QLoRA):**
+  - LoRA attention dimension: 64
+  - Alpha parameter for LoRA scaling: 16
+  - Dropout probability for LoRA layers: 0.1
+
+- **bitsandbytes Parameters:**
+  - Activate 4-bit precision base model loading
+  - Compute dtype for 4-bit base models: float16
+  - Quantization type: nf4
+  - Activate nested quantization for 4-bit base models: No
+
+- **TrainingArguments Parameters:**
+  - Number of training epochs: 1
+  - Batch size per GPU for training: 4
+  - Batch size per GPU for evaluation: 4
+  - Gradient accumulation steps: 1
+  - Enable gradient checkpointing: Yes
+  - Maximum gradient norm: 0.3
+  - Initial learning rate: 2e-4
+  - Weight decay: 0.001
+  - Optimizer: paged_adamw_32bit
+  - Learning rate scheduler type: cosine
+  - Warm-up ratio: 0.03
+  - Group sequences into batches with the same length: Yes
+
+## License
+
+This model is released under the MIT License.