transformers pytorch llama text-generation conversational en dataset:emre/llama-2-instruct-121k-code license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: mit
datasets:
- emre/llama-2-instruct-121k-code
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.