How to build a custom domain specific model for LLMware. Is this something which is in pipeline?

[sandeepanprime]

How to build a custom domain specific model for LLMware. Is this something which is in pipeline?

[KeepALifeUS]

Building custom domain-specific models involves several approaches depending on your needs:

Approaches for Domain Specialization

1. Fine-Tuning (Most Common)

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load base model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3-8B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3-8B")

# Prepare domain-specific dataset
dataset = load_dataset("your_domain_data")

# Fine-tune
training_args = TrainingArguments(
    output_dir="./domain_model",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    learning_rate=2e-5,
    save_strategy="epoch"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset
)

trainer.train()

2. RAG with Domain Knowledge (Lighter Weight)

from llmware.library import Library
from llmware.retrieval import Query

class DomainSpecificRAG:
    def __init__(self, domain_docs_path: str):
        self.library = Library().load(domain_docs_path)
        self.query_engine = Query(self.library)
    
    def answer(self, question: str) -> str:
        # Retrieve domain knowledge
        context = self.query_engine.semantic_search(question, top_k=5)
        
        # Generate with context
        prompt = f"""
Domain Knowledge:
{context}

Question: {question}

Answer based on the domain knowledge above:
"""
        return self.llm.generate(prompt)

3. LoRA Adapters (Efficient Fine-Tuning)

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply LoRA to base model
model = get_peft_model(base_model, lora_config)
# Train only adapter weights (much smaller)

Decision Framework

Need	Approach	Effort
Domain vocabulary	RAG	Low
Domain reasoning	Fine-tune	High
Domain style	LoRA	Medium
Factual accuracy	RAG	Low

For LLMWare Integration

1. Start with RAG using domain documents
2. If accuracy insufficient, add LoRA fine-tuning
3. Use LLMWare SLIM models as efficient base

# Use SLIM model as base
from llmware.models import ModelCatalog

model = ModelCatalog().load_model("slim-summary-tool")
# Fine-tune on domain data

More patterns: https://github.com/KeepALifeUS/autonomous-agents