Update README.md

README.md

@@ -1,199 +1,76 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- rojas-diego/Apple-MLX-QA
+language:
+- en
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+pipeline_tag: question-answering
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# Meta-Llama-3.1-8B-Instruct-Apple-MLX
+
+## Overview
+
+This model is a fine-tuned version of Meta's LLaMa 3.1 8B model, specifically adapted to answer questions and provide guidance on Apple's latest machine learning framework, MLX. The fine-tuning was done using the LORA (Low-Rank Adaptation) method on a custom dataset of question-answer pairs derived from the MLX documentation.
+
+## Dataset
+
+Fine-tuned on a single epoch of [Apple MLX QA](https://huggingface.co/datasets/rojas-diego/Apple-MLX-QA).
+
+## Installation
+
+To use the model, you need to install the required dependencies:
+
+```bash
+pip install peft transformers jinja2==3.1.0
+```
+
+## Usage
+
+Here’s a sample code snippet to load and interact with the model:
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the base model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B-Instruct", torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
+
+# Load the fine-tuned model using LORA
+model = PeftModel.from_pretrained(
+    model,
+    "rojas-diego/Meta-Llama-3.1-8B-Instruct-Apple-MLX",
+).to("cuda")
+
+# Define input using a chat template with a system prompt and user query
+ids = tokenizer.apply_chat_template(
+    [
+        {
+            "role": "system",
+            "content": "You are a helpful AI coding assistant with expert knowledge of Apple's latest machine learning framework: MLX. You can help answer questions about MLX, provide code snippets, and help debug code.",
+        },
+        {
+            "role": "user",
+            "content": "How do you transpose a matrix in MLX?",
+        },
+    ],
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to("cuda")
+
+# Generate and print the response
+print(
+    tokenizer.decode(
+        model.generate(input_ids=ids, max_new_tokens=256, temperature=0.5).tolist()[0][
+            len(ids) :
+        ]
+    )
+)
+```