qwen-2.5-3b-coursezy-assistant

Coach Navigation Assistant — Fine-Tuned Model

This model is fine-tuned to help coaches navigate a dashboard by generating helpful paragraphs that include correct internal HTML links (e.g., <a href="/courses/add">Click here</a>). It is designed for use inside LMS/admin dashboards where users need quick instructions and direct links.

Model Details

Model Description

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct. Its purpose is to understand natural language questions such as:

• "Where do I go to create a course?"
• "How can I add a new video section?"
• "Where do I manage students?"

And return a friendly paragraph that explains the location and includes the correct internal endpoint as a clickable HTML link.

• Developed by: Ziyad T.
• Model type: Instruction-tuned generative model
• Languages: English
• Finetuned from: meta-llama/Llama-3.2-3B-Instruct
• License: Apache 2.0 (same as base model)

Model Sources

• Repository: [This Hugging Face model page]
• Demo: [Optional - Add your demo link]
• Training Notebook: [Add your Colab/Kaggle link]

Uses

Direct Use

The model can be used to:

• Generate dashboard navigation guidance
• Provide contextual instructions
• Embed HTML links inside responses
• Power chatbots for course creators, admins, or coaches

Downstream Use

• LMS support assistants
• Platform onboarding bots
• Context-aware help centers
• Interactive documentation systems

Out-of-Scope Use

The model is NOT suitable for:

• Factual Q&A outside the navigation domain
• Sensitive or medical advice
• Arbitrary text generation not related to navigation
• Producing links outside your controlled system
• General-purpose conversation

Bias, Risks, and Limitations

⚠️ Important Limitations:

• The model will only work correctly with endpoints it was trained on
• If the user asks about a route not in the dataset, the model may guess or hallucinate a link
• The model assumes the platform uses HTML <a> tags — not markdown or other formats
• Responses are optimized for coach/admin users, not students or general users

Recommendations

• ✅ Validate model outputs before exposing them publicly
• ✅ Keep endpoints consistent with the dataset
• ✅ Add more training examples as your app grows
• ✅ Implement fallback responses for unknown queries
• ✅ Monitor and log model outputs for quality assurance

How to Use the Model

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/llama-3.2-3b-coach-assistant")
model = AutoModelForCausalLM.from_pretrained("your-username/llama-3.2-3b-coach-assistant")

# Prepare input
prompt = "Where do I go to create a new course?"

# Generate response
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)

# Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Using with Hugging Face Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/your-username/llama-3.2-3b-coach-assistant"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "Where do I go to create a new course?",
    "parameters": {
        "max_new_tokens": 150,
        "temperature": 0.7,
    }
})

print(output)

Using with Inference Endpoints (Recommended for Production)

import requests

# Your deployed endpoint URL
API_URL = "https://your-endpoint.aws.endpoints.huggingface.cloud"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

payload = {
    "inputs": "Where do I go to create a new course?",
    "parameters": {
        "max_new_tokens": 150,
        "temperature": 0.7,
        "top_p": 0.9,
    }
}

response = requests.post(API_URL, headers=headers, json=payload)
print(response.json())

Training Details

Training Data

A custom JSONL dataset consisting of input-output pairs:

{
  "input": "Where do I go to create a new course?",
  "output": "To create a new course, navigate to the course creation page where you can add all the details about your course. <a href=\"/courses/add\">Click here</a> to start creating your course."
}

Dataset Statistics:

• Total examples: 50+
• Endpoints covered: 8+ major routes
• Variations per endpoint: 3-5 different question phrasings

Covered Endpoints:

• /courses/add - Creating new courses
• /courses/index - Viewing and managing courses
• /inbox - Checking messages
• /accont - Managing profile
• /coach/dashboard - Accessing dashboard
• Course editing, sections, students, and more

Training Procedure

Fine-tuning Method: QLoRA (4-bit quantization + LoRA)

Training Framework:

• TRL SFTTrainer (Supervised Fine-Tuning)
• PEFT (Parameter-Efficient Fine-Tuning)
• BitsAndBytes (4-bit quantization)

Training Configuration:

• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Quantization: 4-bit NF4 with double quantization
• LoRA Rank (r): 16
• LoRA Alpha: 32
• LoRA Dropout: 0.05
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Learning Rate: 2e-4
• Batch Size: 4 per device
• Gradient Accumulation Steps: 4 (effective batch size: 16)
• Epochs: 3
• Max Sequence Length: 512
• Optimizer: paged_adamw_8bit
• LR Scheduler: Cosine
• Warmup Steps: 50
• Mixed Precision: FP16

Speeds, Sizes, Times

Model Size:

• Base model parameters: ~3B
• Trainable parameters (LoRA): ~0.5% of total
• Final model size: ~6GB (merged)

Training Environment:

• GPU: [Add your GPU type, e.g., NVIDIA A100, T4]
• Training time: [Add your training time, e.g., ~2 hours]
• Cloud platform: [Add if applicable, e.g., Google Colab, Kaggle, AWS]

Evaluation

Testing Data

A validation set with unseen question variations for each endpoint to test generalization.

Metrics

Evaluation Criteria:

• ✅ Link Correctness: Does the model return the correct endpoint?
• ✅ Response Quality: Is the paragraph helpful and natural?
• ✅ Format Consistency: Does it follow the HTML link format?
• ✅ Instruction Clarity: Are the instructions clear and actionable?

Results

The model reliably returns:

• ✅ The correct link for trained endpoints
• ✅ Relevant and contextual instructions
• ✅ Consistent HTML formatting
• ✅ Natural, conversational language

Sample Outputs:

Environmental Impact

Carbon Emissions: [Optional - Add if you tracked this]

Fine-tuning was performed using cloud GPU resources. The use of QLoRA (4-bit quantization) significantly reduced computational requirements compared to full fine-tuning.

Technical Specifications

Model Architecture

• Architecture: Llama 3.2 (decoder-only transformer)
• Parameters: ~3 billion
• Attention: Multi-head attention with grouped-query attention
• Context Length: 8192 tokens (base model capability)
• Training Context: 512 tokens (for efficiency)

Compute Infrastructure

• Hardware: [Add your GPU type]
• Software:
  • Python 3.10+
  • PyTorch 2.0+
  • Transformers 4.35+
  • PEFT 0.6+
  • BitsAndBytes 0.41+
  • TRL (Transformer Reinforcement Learning)

Citation

If you use this model in your work, please cite:

BibTeX:

@misc{coach-assistant-2024,
  author = {Ziyad T.},
  title = {Coach Navigation Assistant - Fine-Tuned Llama 3.2 3B},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/your-username/llama-3.2-3b-coach-assistant}}
}

APA:

Ziyad T. (2024). Coach Navigation Assistant - Fine-Tuned Llama 3.2 3B. 
Hugging Face. https://huggingface.co/your-username/llama-3.2-3b-coach-assistant

Model Card Authors

Ziyad T.

Model Card Contact

For questions, issues, or feedback:

• GitHub: [Add your GitHub profile]
• Email: [Add your email]
• Hugging Face: [Add your HF profile]

Acknowledgments

• Base model: Meta AI (Llama 3.2)
• Training framework: Hugging Face (Transformers, PEFT, TRL)
• Quantization: BitsAndBytes team

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Last Updated: November 2024