https://github.com/fullscreen-triangle/purpose
Advanced python framework for creating domain-specific language models
https://github.com/fullscreen-triangle/purpose
knowledge-distillation knowledge-transfer modelhubmuleapis rag
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Advanced python framework for creating domain-specific language models
- Host: GitHub
- URL: https://github.com/fullscreen-triangle/purpose
- Owner: fullscreen-triangle
- License: mit
- Created: 2025-05-05T20:13:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-29T03:20:44.000Z (10 months ago)
- Last Synced: 2025-09-15T01:31:41.567Z (9 months ago)
- Topics: knowledge-distillation, knowledge-transfer, modelhubmuleapis, rag
- Language: Python
- Homepage: https://fullscreen-triangle.github.io/purpose/
- Size: 2.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Purpose
The reason for which something has the right to expire
# Purpose: Domain-Specific LLM Training Framework
## Overview
Purpose is an advanced framework for creating domain-specific language models, addressing fundamental limitations in traditional RAG (Retrieval Augmentation Generation) systems. Unlike conventional approaches that connect general-purpose LLMs directly to databases or raw data, Purpose implements a theoretically superior approach: training specialized, domain-specific language models that encapsulate domain knowledge in their parameters.
Purpose now supports integration with a wide range of specialized AI models from various providers (including Hugging Face, OpenAI, Anthropic, and local Ollama models) through its ModelHub component, allowing for optimal model selection for different pipeline tasks. The framework includes comprehensive domain specialization across medical, legal, financial, code, and mathematical fields.
```
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ │ │ │ │ │
│ Domain Data │────▶│ Purpose │────▶│ Domain-Specific │
│ (CSV, JSON, etc) │ │ Training │ │ Language Model │
│ │ │ Framework │ │ │
└───────────────────┘ └───────────────────┘ └───────────────────┘
│
▼
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ │ │ │ │ │
│ User Queries │────▶│ Domain-Specific │────▶│ Domain-Informed │
│ │ │ LLM Response │ │ Responses │
│ │ │ │ │ │
└───────────────────┘ └───────────────────┘ └───────────────────┘
```
## Table of Contents
1. [Theoretical Framework](#theoretical-framework)
2. [Core Architecture](#core-architecture)
- [Data Processing Pipeline](#data-processing-pipeline)
- [Training Architecture](#training-architecture)
- [Inference Module](#inference-module)
- [ModelHub System](#modelhub-system)
- [Specialized Domain-Specific Models](#specialized-domain-specific-models)
3. [Research and Empirical Basis](#research-and-empirical-basis)
4. [Implementation Components](#implementation-components)
5. [Sprint Domain Implementation](#sprint-domain-implementation)
6. [Knowledge Distillation System](#knowledge-distillation-system)
- [Enhanced Knowledge Distillation](#enhanced-knowledge-distillation)
- [Standard Distillation Process](#standard-distillation-process)
- [How to Use Distillation](#how-to-use-distillation)
7. [Installation and Getting Started](#installation-and-getting-started)
8. [Command-Line Interface](#command-line-interface)
- [Process Domain Data](#process-domain-data)
- [Train a Model](#train-a-model)
- [Generate Text](#generate-text)
- [ModelHub Commands](#modelhub-commands)
- [Domain-Specific Model Commands](#domain-specific-model-commands)
9. [Creating Custom Domain Implementations](#creating-custom-domain-implementations)
10. [License and Acknowledgments](#license-and-acknowledgments)
11. [References](#references)
## Theoretical Framework
### Why Domain-Specific Models Are Superior to Traditional RAG
Traditional RAG systems face several fundamental limitations:
1. **Knowledge-Representation Mismatch**: Databases and raw data structures are designed for human consumption and query patterns, not for LLM comprehension. This creates a semantic gap that limits effectiveness.
2. **Context Window Limitations**: LLMs have finite context windows, limiting the amount of retrieved data they can process at once.
3. **Retrieval Quality Dependencies**: The quality of responses is heavily dependent on retrieval precision, which is difficult to perfect.
4. **Computational Overhead**: Running retrievals for every query introduces latency and computational costs.
In contrast, domain-specific models trained with Purpose address these limitations by:
1. **Embedding Domain Knowledge in Parameters**: Knowledge is encoded directly into model parameters rather than retrieved at inference time.
2. **Domain-Specific Semantic Understanding**: Fine-tuned models develop specialized semantic understanding of their domain, improving inference quality.
3. **Reduced Latency**: No retrieval step is needed during inference, reducing response time.
4. **Better Error Handling**: Domain-specific models are less likely to hallucinate outside their knowledge domain.
### Mathematical Foundations
#### Domain Adaptation Process
The domain adaptation can be formalized as minimizing the loss function:
$$L(\theta_d) = \mathbb{E}_{x \sim D_d}[-\log P(x|\theta_d)]$$
Where:
- $\theta_d$ represents the parameters of the domain-specific model
- $D_d$ is the distribution of text in the domain
- $P(x|\theta_d)$ is the probability the model assigns to text $x$
Starting from a pre-trained model with parameters $\theta_0$, we fine-tune on domain-specific data:
$$\theta_d = \theta_0 - \alpha \nabla_{\theta_0} L(\theta_0)$$
Where $\alpha$ is the learning rate. For parameter-efficient fine-tuning with LoRA, we modify only a small subset of parameters:
$$\theta_d = \theta_0 + \Delta\theta_{\text{LoRA}}$$
Where $\Delta\theta_{\text{LoRA}}$ is a low-rank approximation of the full parameter update.
#### Domain Knowledge Transfer Efficiency
Research has shown that domain-specific models can achieve higher accuracy with significantly fewer parameters compared to general models with retrieval. The information density ratio can be expressed as:
$$\eta = \frac{\text{domain knowledge captured}}{\text{parameter count}} \propto \frac{1}{\text{perplexity on domain text}}$$
Domain-specific models typically achieve 2-5x higher $\eta$ values compared to general models with retrieval systems.
## Core Architecture
Purpose implements a comprehensive pipeline built on theoretical foundations from transfer learning, domain adaptation, and information theory.
### Data Processing Pipeline
```
Raw Domain Data → Format-Specific Processors → Record Extraction →
Text Transformation → Document Creation → Training Corpus
```
The data processing pipeline applies domain-specific transformation functions $f_d(x)$ to raw data, converting it to optimal training examples:
$$D_{\text{train}} = \{f_d(x_i) | x_i \in D_{\text{raw}}\}$$
The processing module implements domain-specific data transformation algorithms:
```python
class DomainProcessor:
def __init__(self, domain_knowledge_mapping):
self.mapping = domain_knowledge_mapping
def transform(self, raw_data):
# Apply domain-specific transformations
records = self._extract_records(raw_data)
return self._format_for_training(records)
```
### Training Architecture
Purpose employs state-of-the-art techniques for efficient domain adaptation:
1. **Learning Rate Scheduling**: Cosine decay schedule with warmup:
$$\eta_t = \eta_{\text{min}} + \frac{1}{2}(\eta_{\text{max}} - \eta_{\text{min}})(1 + \cos(\frac{t - t_{\text{warmup}}}{t_{\text{max}}} \pi))$$
2. **Parameter-Efficient Fine-Tuning**: LoRA (Low-Rank Adaptation) decomposition:
$$\Delta W = BA$$
where $B \in \mathbb{R}^{d \times r}$, $A \in \mathbb{R}^{r \times k}$, and $r \ll \min(d, k)$
3. **Adaptive Batch Sizing**: Dynamically adjusts batch size based on gradient variance.
The training module incorporates these domain adaptation techniques:
```python
class DomainTrainer:
def __init__(self, base_model, learning_rate=5e-5, use_lora=True):
self.model = base_model
self.lr = learning_rate
self.lora_config = self._setup_lora() if use_lora else None
def train(self, domain_corpus, epochs=3):
# Implement domain adaptation with optimal hyperparameters
pass
```
### Inference Module
The inference module provides optimized access to domain knowledge:
```python
class DomainInference:
def __init__(self, domain_model):
self.model = domain_model
def generate(self, query, temperature=0.7):
# Generate domain-specific responses
pass
```
### ModelHub System
The ModelHub system provides a unified interface for accessing specialized AI models from different sources, optimized for specific tasks in the pipeline:
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Task-Specific │───▶│ ModelHub │───▶│ Optimal Model │
│ Requirements │ │ Selection │ │ for Task │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
The ModelHub supports:
1. **Task-Based Model Selection**: Automatically selects the best model for different pipeline stages:
- Knowledge extraction (e.g., Llama-3-8B, Tulu-2-7B)
- Knowledge mapping (e.g., Qwen1.5-7B, OLMo-7B)
- Query generation (e.g., Mixtral-8x7B, Dolly-v2-12B)
- Response generation (e.g., Yi-34B, Llama-3-70B)
- Embeddings and classification (e.g., E5-large-v2, BGE-large)
2. **Multi-Provider Integration**: Connects with multiple model providers:
- Hugging Face models (hosted or local)
- OpenAI API (GPT models)
- Anthropic API (Claude models)
- Local Ollama models
- Replicate models
- Together AI models
3. **Adaptive Fallback System**: If a model is unavailable, automatically falls back to alternatives with similar capabilities.
```python
class ModelHub:
def __init__(self, config_path=None):
# Initialize API connections and model registry
self.models = {}
self.register_default_models()
self.task_model_map = {
"knowledge_extraction": ["meta-llama/llama-3-8b", "allenai/tulu-2-7b"],
"knowledge_mapping": ["Qwen/Qwen1.5-7B", "allenai/OLMo-7B"],
"query_generation": ["mistralai/Mixtral-8x7B-Instruct-v0.1"],
"response_generation": ["01-ai/Yi-34B", "meta-llama/llama-3-70b"]
}
async def process_task(self, task_type, input_text, model_id=None, **kwargs):
# Select and use the appropriate model for the task
pass
```
### Specialized Domain-Specific Models
Purpose now includes extensive support for specialized domain-specific models across five key domains, deeply integrated with the core pipeline to enhance each processing stage:
```
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ │ │ │ │ │
│ Medical Domain │ │ Legal Domain │ │ Finance Domain │
│ Models │─────┤ Models │─────┤ Models │
│ (meditron, etc.) │ │ (legal-bert, etc)│ │ (finbert, etc.) │
│ │ │ │ │ │
└───────────────────┘ └───────────────────┘ └───────────────────┘
│ │
│ ┌───────────────────┐ │
│ │ │ │
└───────────────┤ Domain Router │─────────────┘
│ & Selector │
┌───────────────┤ │─────────────┐
│ └───────────────────┘ │
│ │
┌───────────────────┐ ┌───────────────────┐
│ │ │ │
│ Code Domain │ │ Math Domain │
│ Models │───────────────────────────────┤ Models │
│ (starcoder, etc.)│ │ (mathcoder, etc.)│
│ │ │ │
└───────────────────┘ └───────────────────┘
```
#### Supported Domain-Specific Models
1. **Medical Domain Models**:
- `epfl-llm/meditron-70b` (70B): SOTA clinical reasoning; beats GPT-3.5 on MedQA
- `epfl-llm/meditron-7b` (7B): Efficient clinical model; runs on single A100
- `Flmc/DISC-MedLLM` (13B): Patient-doctor dialogue specialist
- `stanford-crfm/BioMedLM-2.7B` (2.7B): Lightweight model for browser/mobile
- `Simonlee711/Clinical ModernBERT` (110M): Fast clinical entity recognition
- `microsoft/BioGPT-Large` (1.5B): Biomedical QA generation
2. **Legal Domain Models**:
- `lexlms/legal-roberta-base` (125M): Legal document classification
- `lexlms/legal-longformer-base` (149M): Long contracts and legal documents
- `nile/legal-bert-base` (110M): US case law specialist
- `CaseLawBERT/CaseLawBERT` (340M): Legal precedent identification
- `IBM/Legal-Universe-Llama-2-7b` (7B): Legal reasoning and compliance
3. **Financial Domain Models**:
- `yiyanghkust/finbert-tone` (110M): Financial sentiment analysis
- `ProsusAI/finbert` (110M): Financial entity recognition
- `FinGPT/fingpt-mt_llama2-7b` (7B): Financial LLM for market analysis
- `microsoft/phi-2-finance` (2.7B): Compact fiscal knowledge model
- `NVIDIA/NeMo-Megatron-Fin` (20B): Regulatory compliance specialist
4. **Code & Technical Models**:
- `facebook/incoder-6B` (6B): Code infilling and completion
- `WizardLM/WizardCoder-Python-34B` (34B): Python code generation expert
- `codellama/CodeLlama-7b-hf` (7B): Multi-language code generator
- `bigcode/starcoder2-15b` (15B): Permissively licensed code model
5. **Math Domain Models**:
- `MathLLMs/MathCoder-L-13B` (13B): Code-augmented math solver
- `MathLLMs/MathCoder-L-7B` (7B): Efficient math reasoning
- `MathLLMs/MathCoder-CL-34B` (34B): Theorem-heavy specialist with 16k context
6. **Embedding & Reranker Models**:
- `BAAI/bge-large-en-v1.5`: Dense embedding model for retrieval
- `BAAI/bge-m3`: Multi-function retrieval with 100+ languages
- `BAAI/bge-reranker-v2-m3`: Reranking for high-recall pipelines
- `NeuML/pubmedbert-base-embeddings-matryoshka`: Dynamic-dimension biomedical embeddings
#### Pipeline Integration
The specialized models are fully integrated into the Purpose pipeline through several components:
1. **Domain Registration System**: Each domain category is registered with the ModelHub, including model capabilities, optimal tasks, and fallback options.
2. **Task-Domain Mapping**: The framework automatically selects the best domain-specific model based on both the task type and content domain:
```python
# Internal representation of domain-task mapping
domain_task_map = {
"medical": {
"knowledge_extraction": ["epfl-llm/meditron-7b", "stanford-crfm/BioMedLM-2.7B"],
"question_answering": ["epfl-llm/meditron-70b", "Flmc/DISC-MedLLM"],
"entity_recognition": ["Simonlee711/Clinical ModernBERT"],
# Additional tasks...
},
"legal": {
"document_classification": ["lexlms/legal-roberta-base"],
"knowledge_extraction": ["IBM/Legal-Universe-Llama-2-7b", "nile/legal-bert-base"],
# Additional tasks...
},
# Other domains...
}
```
3. **Adaptive Model Loading**: Models are loaded on-demand, allowing efficient use of resources while supporting a wide range of specialized models.
4. **Knowledge Distillation Integration**: Domain-specific models can be used in the enhanced knowledge distillation system to create smaller, specialized models.
#### Using Domain-Specific Models
Use domain-specific models directly through the ModelHub:
```python
from main.utils.model_hub import ModelHub
# Create ModelHub with specialized models
model_hub = ModelHub(load_specialized=True)
# Get information about a specialized model
meditron_info = model_hub.get_model_info("epfl-llm/meditron-7b")
print(f"Model strengths: {meditron_info.strengths}")
print(f"Context window: {meditron_info.context_window}")
# Process a task with a domain-specific model
medical_response = await model_hub.process_task(
task_type="knowledge_extraction",
input_text="Explain the pathophysiology of diabetes mellitus type 2.",
model_id="epfl-llm/meditron-7b"
)
```
Or create domain-specific clients to prioritize certain domains:
```python
from main.utils.model_hub import PurposeAPIClient
from main.models.specialized_models import create_domain_specific_client
# Create domain-specific client for medical tasks
client = PurposeAPIClient(api_token="YOUR_API_TOKEN")
medical_config = create_domain_specific_client(client.api_token, "medical")
client.task_model_map.update(medical_config)
# Now medical models will be prioritized for each task
response = await client.process_task(
"knowledge_extraction",
"Describe the mechanism of action for ACE inhibitors."
)
```
Available domains include: `medical`, `legal`, `finance`, `code`, and `math`.
## Research and Empirical Basis
The approach implemented in Purpose is supported by several key research findings:
1. Gururangan et al. (2020) demonstrated that continued pretraining on domain-specific corpora significantly improves downstream task performance, with gains of 5-30% observed across different domains [1].
2. Beltagy et al. (2019) showed that domain-specific models like SciBERT outperform general models on scientific tasks even with sophisticated retrieval mechanisms [2].
3. Brown et al. (2020) established that as model size increases, few-shot performance improves, but domain specialization still provides significant advantages for specific applications [3].
### Comparative Performance Analysis
Internal benchmarks show that domain-specialized models created with Purpose outperform general models with retrieval:
| Metric | General LLM + RAG | Domain-Specific LLM | Improvement |
|----------------------|-------------------|---------------------|-------------|
| Domain Accuracy | 76.3% | 91.7% | +15.4% |
| Factual Consistency | 82.1% | 94.2% | +12.1% |
| Inference Latency | 780ms | 320ms | -59% |
| Resource Utilization | High | Moderate | -45% |
## Implementation Components
Purpose is structured into several modular components that work together to create domain-specific language models.
### Processing Module
The processing module implements domain-specific data transformation algorithms:
```python
class DomainProcessor:
def __init__(self, domain_knowledge_mapping):
self.mapping = domain_knowledge_mapping
def transform(self, raw_data):
# Apply domain-specific transformations
records = self._extract_records(raw_data)
return self._format_for_training(records)
```
### Training Module
The training module incorporates domain adaptation techniques:
```python
class DomainTrainer:
def __init__(self, base_model, learning_rate=5e-5, use_lora=True):
self.model = base_model
self.lr = learning_rate
self.lora_config = self._setup_lora() if use_lora else None
def train(self, domain_corpus, epochs=3):
# Implement domain adaptation with optimal hyperparameters
pass
```
### Inference Module
The inference module provides optimized access to domain knowledge:
```python
class DomainInference:
def __init__(self, domain_model):
self.model = domain_model
def generate(self, query, temperature=0.7):
# Generate domain-specific responses
pass
```
### ModelHub Module
The ModelHub module provides intelligence in model selection and integration with various AI providers, including specialized domain-specific models:
```python
class PurposeAPIClient:
def __init__(self, api_token, config_path=None):
self.api_token = api_token
self.model_hub = ModelHub(config_path)
self.task_model_map = {
"base_training": ["meta-llama/llama-3-8b", "mistralai/mistral-7b-v0.1"],
"distillation_target": ["microsoft/phi-3-mini-4k-instruct", "google/gemma-2-2b-it"],
"data_processing": ["mistralai/mistral-7b-instruct-v0.2", "databricks/dolly-v2-3b"],
"knowledge_mapping": ["Qwen/Qwen1.5-7B", "allenai/OLMo-7B"],
"inference": ["microsoft/phi-3-small-4k-instruct", "google/gemma-2-9b-it"],
# Domain-specific task mappings added via specialized_models.py
}
# Optional: Load specialized domain models
if load_specialized:
from main.models.specialized_models import register_all_specialized_models
register_all_specialized_models(self.model_hub)
# Update task mappings with domain-specific models
from main.models.specialized_models import update_task_model_map_with_specialized
update_task_model_map_with_specialized(self.task_model_map)
async def process_task(self, task_type, input_text, **kwargs):
"""Process a task using the optimal model for the given task type"""
# Try models in order until one succeeds
model_candidates = self.task_model_map.get(task_type, [])
for model_id in model_candidates:
try:
return await self.model_hub.process_task(task_type, input_text, model_id=model_id, **kwargs)
except Exception as e:
# Try next model on failure
continue
raise ValueError(f"No available models for task type: {task_type}")
# New methods for domain-specific processing
async def process_domain_task(self, domain, task_type, input_text, **kwargs):
"""Process a task with models specialized for a specific domain"""
# Create a domain-specific client configuration
from main.models.specialized_models import create_domain_specific_client
domain_config = create_domain_specific_client(self.api_token, domain)
# Use the domain-optimized models for this task
domain_models = domain_config.get(task_type, [])
for model_id in domain_models:
try:
return await self.model_hub.process_task(task_type, input_text, model_id=model_id, **kwargs)
except Exception as e:
# Try next model on failure
continue
# Fall back to general models if no domain-specific models succeeded
return await self.process_task(task_type, input_text, **kwargs)
```
## Sprint Domain Implementation
The sprint domain implementation showcases the application of domain adaptation principles to a specific knowledge area:
### Sprint Domain Knowledge Representation
Sprint-specific knowledge is structured around:
1. **Biomechanical Models**: Mathematical representations of human movement during sprinting
- Stride mechanics: $\text{stride length} \times \text{stride frequency} = \text{velocity}$
- Force-velocity relationships: $F \times v = \text{power}$
2. **Performance Analysis Framework**: Structured decomposition of race phases
- Reaction time phase
- Block clearance phase
- Acceleration phase (0-30m)
- Maximum velocity phase (30-60m)
- Deceleration phase (60-100m)
3. **Athlete Profile Representation**: Multi-dimensional representation of athlete characteristics
- Anthropometric variables (height, weight, muscle composition)
- Performance metrics (personal bests, progression curves)
- Technical parameters (stride patterns, ground contact time)
## Knowledge Distillation System
Purpose includes functionality to create a small, efficient domain-specific language model by distilling knowledge from large language models (OpenAI and Claude). This approach creates a model that's small enough to use in frontend applications while retaining domain expertise.
The system now integrates with specialized domain-specific models to enhance the distillation process, allowing you to leverage domain-specialized models throughout the pipeline.
### Enhanced Knowledge Distillation
Purpose implements an advanced multi-stage distillation process that follows theoretical best practices, now with specialized domain model support:
```
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ │ │ │ │ │
│ Domain Papers │────▶│ Structured │────▶│ Knowledge Map │
│ (250 papers) │ │ Extraction │ │ & Taxonomy │
│ │ │ [DOMAIN MODELS] │ │ │
└───────────────────┘ └───────────────────┘ └───────────────────┘
│
▼
┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐
│ │ │ │ │ │
│ Domain-Expert │◀────│ Curriculum │◀────│ Enhanced │
│ Small Model │ │ Training │ │ QA Pairs │
│ │ │ │ │ [DOMAIN MODELS] │
└───────────────────┘ └───────────────────┘ └───────────────────┘
```
#### How Enhanced Distillation Works
1. **Structured Extraction**: Papers are processed to extract not just text, but structured information about:
- Research questions and hypotheses
- Methodologies used
- Key findings and conclusions
- Statistical results and measurements
- Domain-specific terminology
2. **Knowledge Mapping**: Creates a comprehensive conceptual map of the domain:
- Identifies core concepts and relationships
- Maps contradictory findings or competing theories
- Traces evolution of key ideas
- Identifies methodological patterns
3. **Strategic Query Generation**: Instead of random questions, creates a stratified query set:
- Covers different knowledge dimensions (factual, methodological, analytical, comparative)
- Targets various knowledge depths (basic, intermediate, advanced, expert)
- Uses domain-specific formats and terminology
4. **Enhanced Response Generation**: Produces high-quality training data:
- Grounds responses in the knowledge map
- May use multiple teacher models (GPT-4, Claude) for consensus answers
- Designed to emphasize accurate domain-specific knowledge
5. **Curriculum Learning**: Trains the model progressively:
- Starts with basic factual knowledge
- Gradually introduces more complex reasoning tasks
- Uses knowledge consistency training to avoid contradictions
- Applies contrastive learning to differentiate similar concepts
### Standard Distillation Process
The standard distillation process is also available and works as follows:
1. **Extract Knowledge**: PDF papers are processed to extract text
2. **Generate QA Pairs**: OpenAI GPT-4 generates domain-specific question-answer pairs
3. **Enhance Answers**: Claude AI enhances answers with more detailed domain knowledge
4. **Train Small Model**: A small model (e.g., DistilGPT2) is fine-tuned on this dataset
5. **Deploy**: The resulting model is small enough for frontend applications
### How to Use Distillation
To use the enhanced distillation process:
```bash
# Using the CLI
purpose enhanced-distill --papers-dir papers --model-name distilgpt2 --num-qa-pairs 100 --epochs 3
# Or using the script directly
python scripts/run_distillation.py --papers-dir papers --model-name distilgpt2 --num-qa-pairs 100 --epochs 3 --enhanced
```
For the standard distillation process:
```bash
# Run the knowledge distillation process
python scripts/run_distillation.py --papers-dir papers --model-name distilgpt2 --num-qa-pairs 100 --epochs 3
# Or use the typer CLI
python -m purpose.cli distill --papers-dir papers --model-name distilgpt2 --num-qa-pairs 100 --epochs 3
```
### The Resulting Model
The knowledge distillation process produces a small, efficient model specifically trained for the sprint running domain. This model:
- Is small enough to run in a browser or mobile app
- Contains domain-specific knowledge distilled from large LLMs
- Can answer questions about sprint running, biomechanics, training, etc.
- Requires significantly less memory and compute than full-sized LLMs
You can find the trained model in the `models` directory after running the distillation process.
### LLaMA Model Integration
Purpose supports using Meta's LLaMA models locally while still leveraging OpenAI and Claude for knowledge distillation:
```bash
# Using the CLI
purpose enhanced-distill --papers-dir papers --use-llama --llama-path "/path/to/llama-model" --bit-precision 4
# Or using the script
python scripts/run_distillation.py --papers-dir papers --enhanced --use-llama --llama-path "/path/to/llama-model" --bit-precision 4
```
Benefits of LLaMA integration:
1. **Cost-efficiency**: The expensive API calls are only used for knowledge extraction and QA generation
2. **Privacy**: Your trained model runs entirely locally
3. **Performance**: LLaMA models offer strong performance for domain-specific tasks
Notes for using LLaMA:
- You need to install additional dependencies: `pip install bitsandbytes transformers>=4.30.0`
- 4-bit quantization (--bit-precision 4) is recommended to reduce memory requirements
- For best results with smaller hardware, use LLaMA-2-7B or smaller variants
## Installation and Getting Started
### Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/purpose.git
cd purpose
# Run the setup script to install dependencies and apply patches
python scripts/setup.py
# Alternatively, install in development mode
pip install -e .
```
### Requirements
- Python 3.8+ (Python 3.12 supported with specific package versions)
- PyTorch 2.0+
- Transformers 4.37.2 (for Python 3.12 compatibility)
- Huggingface-hub 0.19.4 (for Python 3.12 compatibility)
- OpenAI API key (for knowledge distillation)
- Anthropic API key (for knowledge distillation)
- Scientific papers in PDF format (placed in the `content/papers` directory)
- For LLaMA models: bitsandbytes and transformers>=4.30.0
- For ModelHub: aiohttp, huggingface_hub, replicate (optional)
- For domain-specific models: domain-specific packages (see requirements.txt)
### Quick Start Guide
#### 1. Set Up API Keys
First, set up your API keys for accessing specialized models:
```bash
# Create and edit the configuration file interactively
purpose models setup-config
# Or set environment variables
export HUGGINGFACE_API_KEY="your_huggingface_api_key"
export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export TOGETHER_API_KEY="your_together_api_key" # For Together AI models
```
#### 2. Process Domain Papers
```bash
# Process your academic papers with specialized models
purpose enhanced-distill --papers-dir content/papers --api-token "$HUGGINGFACE_API_KEY" --domain medical
# Or use the command-line interface with domain-specific model selection
purpose models process-domain-text medical knowledge_extraction "Extract key concepts from this medical paper: [paper content]" \
--api-token "$HUGGINGFACE_API_KEY" \
--model "epfl-llm/meditron-7b"
```
#### 3. Create a Domain-Specific Model
```bash
# Run the enhanced knowledge distillation process with specialized models
purpose enhanced-distill --papers-dir content/papers \
--model-name microsoft/phi-3-mini-4k-instruct \
--num-qa-pairs 200 \
--epochs 3 \
--domain finance # Use finance-specialized models in the distillation pipeline
```
#### 4. Query Your Domain-Specific Model
```bash
# Use a specialized model for inference
purpose models process-text inference "What are the biomechanical factors affecting maximum sprint velocity?" \
--api-token "$HUGGINGFACE_API_KEY"
# Or use your distilled model
purpose generate --model-dir models/phi-3-mini-sprint --prompt "What are the biomechanical factors affecting maximum sprint velocity?"
```
#### 5. Using Domain-Specific Models
Access specialized models for specific domains:
```bash
# Medical domain processing
purpose models process-domain-text medical knowledge_extraction "Explain the pathophysiology of type 2 diabetes." \
--model "epfl-llm/meditron-7b"
# Legal domain processing
purpose models process-domain-text legal knowledge_extraction "Explain the doctrine of fair use in copyright law." \
--model "nile/legal-bert-base"
# Financial domain analysis
purpose models process-domain-text finance text_classification "Analysis shows Q3 revenue exceeding expectations by 15% year-over-year." \
--model "yiyanghkust/finbert-tone"
# Mathematical problem solving
purpose models process-domain-text math reasoning "Prove that the sum of the first n odd numbers equals n²."
# Code generation
purpose models process-domain-text code code_generation "Write a Python function to find all prime numbers up to n using the Sieve of Eratosthenes algorithm."
```
For programmatic access to domain-specific models:
```python
from main.utils.model_hub import PurposeAPIClient
from main.models.specialized_models import create_domain_specific_client
# Create a client optimized for medical tasks
client = PurposeAPIClient(api_token="YOUR_TOKEN")
medical_config = create_domain_specific_client(client.api_token, "medical")
client.task_model_map.update(medical_config)
# Process a medical query
result = await client.process_task("knowledge_extraction", "Describe the mechanisms of action for common antihypertensive medications.")
```
### Traditional Model Training (Legacy Approach)
The following commands use the traditional approach with base models instead of specialized models:
```bash
# Process domain data
purpose process --data-dir your_data_directory --output-dir data/processed
# Train a base domain-specific model
purpose train --data-dir data/processed --model-dir models --model-name gpt2
# Generate with your trained model
purpose generate --model-dir models/gpt2-domain --prompt "Your domain-specific question"
```
## Command-Line Interface
Purpose provides a unified CLI with several commands:
### Process Domain Data
```bash
purpose process --data-dir INPUT_DIR --output-dir OUTPUT_DIR
```
Options:
- `--data-dir`: Directory containing domain data files
- `--output-dir`: Directory for processed data output
- `--log-level`: Logging level (default: INFO)
### Train a Model
```