rohith-yarramala/asyncapi-assistant-model-merged

AsyncAPI Assistant Model

Model Details

Model Name: AsyncAPI Assistant Model
Developed by: Rohith Yarramala Organization: AsyncAPI
Language(s): English
License: Apache 2.0
Fine-tuned from: DeepSeek Code Model (deepseek_v2)
Model Type: Transformer-based Conversational AI Model
Precision: 4-bit (Optimized with BitsandBytes)

Model Description

The AsyncAPI Assistant Model is an advanced AI-powered assistant designed to provide high-accuracy responses for AsyncAPI-related queries. The model has been fine-tuned using a comprehensive dataset, including AsyncAPI documentation, event-driven architecture code, GitHub discussions, Stack Overflow threads, and related technical resources. It supports conversational interactions, text-to-code generation, debugging, and AI-driven information retrieval.

Model Sources

Repository: Hugging Face Model Repo
Paper (if applicable): N/A
Demo: Coming soon

Uses

Direct Use

• Answering questions related to AsyncAPI specifications and event-driven architecture.
• Generating code snippets and templates for AsyncAPI-based implementations.
• Debugging and reviewing AsyncAPI-related code.
• Assisting developers in understanding and working with AsyncAPI standards.

Downstream Use

• Integration with AsyncAPI Studio for AI-driven support.
• Embedding in documentation websites for real-time assistance.
• Deployment in chatbots for API-related support.

Out-of-Scope Use

• Not suitable for general-purpose NLP tasks outside the AsyncAPI domain.
• Should not be used for sensitive or mission-critical applications without validation.

Bias, Risks, and Limitations

• The model is trained on publicly available AsyncAPI resources and may inherit biases from the source data.
• It may generate incorrect or outdated information if the documentation changes over time.
• Performance may vary depending on the complexity of queries.

Recommendations

• Users should verify the responses against the latest AsyncAPI documentation.
• Developers integrating this model should provide fallback mechanisms for cases where human intervention is required.

How to Get Started

You can use the following Python code snippet to start using the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "rohith-yarramala/asyncapi-assistant-model-merged"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    output = model.generate(**inputs, max_new_tokens=200)
    return tokenizer.decode(output[0], skip_special_tokens=True)

print(generate_response("What is AsyncAPI?"))

Training Details

Training Data

• The dataset consists of structured JSON files from AsyncAPI documentation, GitHub discussions, event-driven architecture tutorials, and relevant technical forums.

Training Procedure

• Data was preprocessed to extract relevant sections and structured into a fine-tuning dataset.
• The model was fine-tuned using supervised learning techniques on an 8.3B parameter model with 4-bit quantization for efficient deployment.

Training Hyperparameters

• Optimizer: AdamW
• Batch Size: 32
• Learning Rate: 3e-5
• Training Steps: 50K
• Precision: Mixed FP16/BF16 with 4-bit quantization

Evaluation

Testing Data

• The model was evaluated on real-world AsyncAPI queries, including documentation lookups, code generation, and debugging scenarios.

Metrics

• Accuracy: 92% on benchmarked AsyncAPI queries.
• Response Relevance: 89% based on human evaluation.
• Inference Speed: Optimized for real-time responses on consumer-grade GPUs.

Results

• High accuracy in answering AsyncAPI-related questions.
• Effective in generating an