Create README.md

README.md

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+---
+license: mit
+datasets:
+- starmpcc/Asclepius-Synthetic-Clinical-Notes
+language:
+- en
+---
+## 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")
+
+# Run text generation pipeline with our next model
+prompt = "Write code to do a POST request in FastAPI framework to find the multiplication of two matrices using NumPy"
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500)
+result = pipe(f"[INST] {prompt} [/INST]")
+print(result[0]['generated_text'])
+```
+
+- **Output API Generation:**
+```
+[INST] Write code to do a POST request in fastapi framework to find the multiplication of two matrices using numpy [/INST]
+Below is an example of how to make a POST request in FastAPI to find the multiplication of two matrices using numpy:
+```
+```python
+from fastapi import FastAPI, HTTPException
+import numpy as np
+
+app = FastAPI()
+
+@app.post("/matrix_multiplication")
+async def matrix_multiplication(matrix1: np.ndarray, matrix2: np.ndarray):
+    if matrix1.shape[1]!= matrix2.shape[0]:
+        raise HTTPException(status_code=400, detail="The number of columns in matrix1 must be equal to the number of rows in matrix2")
+    result = np.matmul(matrix1, matrix2)
+    return {"result": result}
+```
+This code defines a FastAPI endpoint at `/matrix_multiplication` that takes two matrices as input and returns the multiplication of the two matrices. The `np.matmul` function is used to perform the multiplication. The endpoint also includes a check to ensure that the number of columns in the first matrix is equal to the number of rows in the second matrix.
+
+To use this endpoint, you can make a POST request to `http://localhost:8000/matrix_multiplication` with the two matrices as input. The response will include the multiplication of the two matrices.
+```python
+import requests
+
+matrix1 = np.array([[1, 2], [3, 4]])
+matrix2 = np.array([[5, 6], [7, 8]])
+
+response = requests.post("http://localhost:8000/matrix_multiplication", json={"matrix1": matrix1, "matrix2": matrix2})
+
+print(response.json())
+```
+This code makes a POST request to the endpoint with the two matrices as input and prints the response. The response should include the multiplication of the two matrices, which is `[[11, 14], [29, 36]]`.
+
+## License
+
+This model is released under the MIT License.