elucidator8918/apigen-prototype-0.1

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):

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:

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.

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.