SLM-Builder: New Features & Enhancements

🎯 Overview

This document describes the major enhancements made to SLM-Builder to support dynamic base model loading from any source and missing features implementation.

✨ New Features

1. Dynamic Multi-Source Model Loading

The ModelRegistry and enhanced ModelFactory now support loading models from multiple sources:

Supported Model Sources:

  1. HuggingFace Hub - Standard and most common source 
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    builder = SLMBuilder(project_name='my-project', base_model='gpt2')
builder = SLMBuilder(project_name='my-project', base_model='meta-llama/Llama-2-7b-hf')
  2. Local Paths - Load from local directories 
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    builder = SLMBuilder(project_name='my-project', base_model='/path/to/local/model')
  3. Ollama Models - Use Ollama-hosted models 
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    builder = SLMBuilder(project_name='my-project', base_model='ollama:llama2')
builder = SLMBuilder(project_name='my-project', base_model='ollama:mistral')
  4. GGUF Files - Load quantized GGUF models 
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    builder = SLMBuilder(project_name='my-project', base_model='/path/to/model.gguf')
builder = SLMBuilder(project_name='my-project', base_model='https://example.com/model.gguf')
  5. HTTP/S3 URLs - Download and load from URLs 
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    builder = SLMBuilder(project_name='my-project', base_model='https://example.com/model.bin')
  6. Model Zoo - Curated collection of popular models 
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    from slm_builder.models import list_models, search_models
   
# List all available models
models = list_models()
   
# Search for specific models
llama_models = search_models("llama", size_filter="7B")
   
# Use by alias
builder = SLMBuilder(project_name='my-project', base_model='mistral-7b')
builder = SLMBuilder(project_name='my-project', base_model='phi-2')

Auto-Detection

The system automatically detects the model source:

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from slm_builder.models import detect_model_source

# Automatically detects source type
source, path = detect_model_source("gpt2")  # -> HuggingFace Hub
source, path = detect_model_source("/local/model")  # -> Local path
source, path = detect_model_source("ollama:mistral")  # -> Ollama
source, path = detect_model_source("model.gguf")  # -> GGUF file

Custom Model Registration

Register your own models:

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from slm_builder.models import register_model, ModelSource

register_model(
    name="my-custom-model",
    source=ModelSource.HUGGINGFACE_HUB,
    path="myorg/my-model",
    size="3.5B",
    description="Custom fine-tuned model"
)

# Now use it
builder = SLMBuilder(project_name='project', base_model='my-custom-model')

2. Advanced Quantization Support

Enhanced quantization options beyond 8-bit:

4-bit Quantization (NF4) 

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builder = SLMBuilder(project_name='my-project', base_model='llama-2-7b')
model, tokenizer = ModelFactory.load_model_and_tokenizer(
    'llama-2-7b',
    load_in_4bit=True  # NF4 quantization
)

Custom Quantization Configuration 

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from transformers import BitsAndBytesConfig

quantization_config = {
    "load_in_4bit": True,
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_use_double_quant": True,
    "bnb_4bit_quant_type": "nf4"
}

model, tokenizer = ModelFactory.load_model_and_tokenizer(
    'llama-2-7b',
    quantization_config=quantization_config
)

Support for GPTQ, AWQ

  • GGUF format support (via llama-cpp-python)
  • Compatible with GPTQ-quantized models from HuggingFace
  • AWQ quantization support through model loading

3. Comprehensive Model Evaluation

New Evaluator class with multiple metrics:

Available Metrics:

  • Perplexity - Language modeling quality
  • BLEU Score - Translation/generation quality
  • ROUGE Scores (ROUGE-1, ROUGE-2, ROUGE-L) - Summarization quality
  • Accuracy - Classification/QA accuracy
  • F1 Score - Precision and recall balance
  • Custom Metrics - User-defined evaluation functions

Usage: 

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from slm_builder.models import Evaluator, evaluate_model

# Quick evaluation
results = evaluate_model(
    model=model,
    tokenizer=tokenizer,
    dataset=test_dataset,
    metrics=['perplexity', 'bleu', 'rouge', 'accuracy'],
    device='cuda'
)

print(results)
# {
#     'perplexity': 12.5,
#     'bleu': 0.65,
#     'rouge': {'rouge1': 0.72, 'rouge2': 0.58, 'rougeL': 0.68},
#     'accuracy': 0.85
# }

# Advanced evaluation with custom metrics
def my_custom_metric(model, dataset):
    # Custom evaluation logic
    return score

evaluator = Evaluator(
    model=model,
    tokenizer=tokenizer,
    device='cuda',
    custom_metrics=[my_custom_metric]
)

results = evaluator.evaluate(dataset, metrics=['perplexity', 'accuracy'])

4. Model Zoo with 20+ Pre-configured Models

Curated collection of popular models across families:

GPT Family

  • gpt2 (124M)
  • gpt2-medium (355M)
  • gpt2-large (774M)
  • distilgpt2 (82M)

Llama Family

  • llama-2-7b (7B)
  • llama-2-13b (13B)
  • llama-3-8b (8B)

Mistral Family

  • mistral-7b (7B)
  • mixtral-8x7b (47B)

Phi Family

  • phi-2 (2.7B)
  • phi-3-mini (3.8B)

Gemma Family

  • gemma-2b (2B)
  • gemma-7b (7B)

Others

  • tinyllama (1.1B)
  • qwen-1.8b (1.8B)
  • qwen-7b (7B)

5. Enhanced Hardware Recommendations

Improved hardware detection and model recommendations:

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from slm_builder.utils import detect_hardware, recommend_base_models

# Detect hardware
hw_profile = detect_hardware()
# {
#     'has_cuda': True,
#     'gpu_count': 1,
#     'gpu_memory_gb': 24.0,
#     'gpu_name': 'NVIDIA RTX 4090',
#     'cpu_count': 16,
#     'ram_gb': 64.0,
#     'platform': 'Linux'
# }

# Get recommendations based on hardware
recommendations = recommend_base_models(hw_profile)
for rec in recommendations:
    print(f"{rec['name']} ({rec['size']}) - {rec['reason']}")

🔧 Implementation Details

New Files Created:

  1. slm_builder/models/registry.py (450+ lines)
    • ModelRegistry class for model discovery
    • Model zoo with 20+ pre-configured models
    • Auto-detection of model sources
    • Custom model registration
    • HuggingFace validation
    • Ollama integration
  2. slm_builder/models/ollama_wrapper.py (180+ lines)
    • OllamaModelWrapper class
    • OllamaTokenizer wrapper
    • CLI integration for Ollama
    • HuggingFace-like API compatibility
  3. slm_builder/models/gguf_wrapper.py (170+ lines)
    • GGUFModelWrapper class
    • GGUFTokenizer wrapper
    • llama-cpp-python integration
    • URL download support
  4. slm_builder/models/evaluation.py (430+ lines)
    • Evaluator class
    • Perplexity computation
    • BLEU score calculation
    • ROUGE score calculation
    • Accuracy and F1 metrics
    • Custom metric support

Enhanced Files:

  1. slm_builder/models/base.py
    • Added multi-source loading support
    • 4-bit/8-bit quantization
    • Custom quantization configs
    • Source routing logic
    • 200+ lines of new code
  2. slm_builder/models/__init__.py
    • Exported new registry functions
    • Exported evaluation classes
    • Updated API surface

📊 Usage Examples

Example 1: Load Different Model Sources 

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from slm_builder import SLMBuilder

# HuggingFace Hub
builder1 = SLMBuilder(project_name='hf-project', base_model='gpt2')

# Local path
builder2 = SLMBuilder(project_name='local-project', base_model='/models/my-model')

# Ollama
builder3 = SLMBuilder(project_name='ollama-project', base_model='ollama:llama2')

# GGUF
builder4 = SLMBuilder(project_name='gguf-project', base_model='model.gguf')

# Model zoo alias
builder5 = SLMBuilder(project_name='zoo-project', base_model='mistral-7b')

Example 2: Train with 4-bit Quantization 

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from slm_builder import SLMBuilder
from slm_builder.models import ModelFactory

# Load model with 4-bit quantization
builder = SLMBuilder(
    project_name='efficient-training',
    base_model='llama-2-7b',
    device='cuda'
)

# Build with LoRA on quantized model
result = builder.build_from_csv(
    path='data.csv',
    task='qa',
    recipe='lora',
    overrides={
        'training': {
            'batch_size': 4,
            'epochs': 3
        }
    }
)

Example 3: Evaluate Model Performance 

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from slm_builder.models import evaluate_model

# Evaluate trained model
results = evaluate_model(
    model=model,
    tokenizer=tokenizer,
    dataset=test_data,
    metrics=['perplexity', 'bleu', 'rouge', 'accuracy', 'f1'],
    device='cuda'
)

print(f"Perplexity: {results['perplexity']:.2f}")
print(f"BLEU: {results['bleu']:.2f}")
print(f"ROUGE-L: {results['rouge']['rougeL']:.2f}")
print(f"Accuracy: {results['accuracy']:.2%}")
print(f"F1 Score: {results['f1']:.2f}")

Example 4: Search and Register Models 

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from slm_builder.models import search_models, register_model, list_models

# Search for 7B models
models_7b = search_models("7b")
for model in models_7b:
    print(f"{model['name']} - {model['size']}")

# Register custom model
register_model(
    name="my-domain-model",
    source="huggingface_hub",
    path="myorg/domain-model-v2",
    size="3B",
    description="Domain-specific fine-tuned model"
)

# List all available models
all_models = list_models()
print(f"Total models available: {len(all_models)}")

🚀 Benefits

1. Flexibility

  • Load models from any source
  • No vendor lock-in
  • Support for local, cloud, and edge models

2. Efficiency

  • 4-bit quantization for 75% memory reduction
  • GGUF support for CPU-optimized inference
  • Ollama integration for fast local deployment

3. Quality Assurance

  • Comprehensive evaluation metrics
  • Support for custom metrics
  • Automated model validation

4. Ease of Use

  • Auto-detection of model sources
  • Model zoo with pre-configured models
  • Hardware-based recommendations

5. Production Ready

  • Error handling for all loaders
  • Logging throughout
  • Type hints and documentation
  • Code quality (black, flake8, isort compliant)

📦 Dependencies

Required for New Features: 

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# Base (already included)
transformers >= 4.30.0
torch >= 2.0.0

# Quantization
bitsandbytes >= 0.41.0  # For 4-bit/8-bit quantization

# GGUF Support
llama-cpp-python >= 0.2.0  # Optional

# Evaluation
nltk >= 3.8  # For BLEU scores
rouge-score >= 0.1.2  # For ROUGE scores
numpy >= 1.24.0  # For metrics computation

# Ollama (system-level)
# Install via: curl https://ollama.ai/install.sh | sh

⚡ Performance

Model Loading Times (approximate):

Source Size Load Time Memory
HF Hub (gpt2) 124M 2-5s 500MB
HF Hub (llama-2-7b) 7B 30-60s 14GB
HF Hub (llama-2-7b, 4-bit) 7B 40-70s 4GB
Local (cached) Any < 2s Varies
GGUF (llama-2-7b-q4) 7B 5-10s 4GB
Ollama (llama2) 7B < 1s* 4GB

*Assuming Ollama model is pre-pulled

🎓 Best Practices

1. Choose the Right Source

  • HuggingFace Hub: Best for training and fine-tuning
  • Ollama: Best for quick local inference and testing
  • GGUF: Best for CPU inference and edge deployment
  • Local Path: Best for production with version control

2. Use Quantization Wisely

  • 4-bit: For large models on consumer GPUs (minimal quality loss)
  • 8-bit: For balanced performance and quality
  • Full precision: For maximum quality and when memory permits

3. Evaluate Thoroughly

  • Use multiple metrics (perplexity + task-specific)
  • Test on held-out test set
  • Compare with baseline models
  • Track metrics across training iterations

🔮 Future Enhancements

Additional features that can be implemented:

  1. Dataset Splitting - Train/val/test split with stratification
  2. Model Comparison - Side-by-side model benchmarking
  3. Database Loaders - SQL, MongoDB integration
  4. API Loaders - REST API data sources
  5. Cross-validation - K-fold validation support
  6. Experiment Tracking - MLflow/Weights & Biases integration

📝 Summary

The SLM-Builder package now supports:

✅ Dynamic model loading from 6+ sources
✅ 20+ pre-configured models in model zoo
✅ 4-bit/8-bit quantization support
✅ Comprehensive evaluation metrics (5+ metrics)
✅ Auto-detection of model sources
✅ Custom model registration
✅ Hardware-based recommendations
✅ Production-ready wrappers for Ollama and GGUF

Total new code: ~2,000 lines across 4 new files and enhancements to existing files.

All code passes: black, flake8, and isort checks. ✨

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