Create README.md

README.md

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+---
+license: mit
+datasets:
+- emre/llama-2-instruct-121k-code
+language:
+- en
+---
+# Model Card for Model ID: [Your Model ID Here]
+
+## Overview
+
+This model, elucidator8918/apigen-prototype-0.1, is tailored for API generation, based on the Mistral-7B-Instruct-v0.1-sharded architecture fine-tuned on the LLAMA-2 Instruct 121k Code dataset.
+
+## Key Information
+
+- **Model Name**: Mistral-7B-Instruct-v0.1-sharded
+- **Fine-tuned Model Name**: elucidator8918/apigen-prototype-0.1
+- **Dataset**: emre/llama-2-instruct-121k-code
+- **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
+
+## Usage
+
+
+- **Example Code (API Generation):**
+
+```python
+from transformers import pipeline
+
+api_gen_pipeline = pipeline("text-generation", model="elucidator8918/apigen-prototype-0.1")
+
+generated_api_code = api_gen_pipeline("Your prompt or input text here", max_length=150, num_return_sequences=1)
+print(generated_api_code)
+```
+
+## License
+
+This model is released under the MIT License.