jacobpmeyer/book-advisor-merged

📚 Jacob's Personal Book Advisor (Merged Model)

This is a merged model combining Llama-3.1-8B-Instruct with a LoRA adapter trained on Jacob's personal book library.

✅ Ready for Inference API - This merged model works directly with HuggingFace Inference API.

Features

• Personalized book recommendations from Jacob's library
• Content questions and summaries
• Reading advice based on actual book collection

Usage

With Inference API

from huggingface_hub import InferenceClient

client = InferenceClient(model="jacobpmeyer/book-advisor-merged")
response = client.text_generation(
    "### Instruction:\nRecommend a science fiction book\n\n### Response:\n",
    max_new_tokens=300,
    temperature=0.7
)
print(response)

Direct Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jacobpmeyer/book-advisor-merged")
model = AutoModelForCausalLM.from_pretrained("jacobpmeyer/book-advisor-merged")

prompt = "### Instruction:\nWhat's a good book for vacation reading?\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Details

• Base Model: meta-llama/Llama-3.1-8B-Instruct
• Method: LoRA fine-tuning + merging
• Training Data: Personal epub book collection
• Format: Instruction-following (Alpaca style)

Model Performance

This merged model combines the instruction-following capabilities of Llama-3.1-8B-Instruct with personalized knowledge from Jacob's book library, providing relevant and personalized book recommendations and insights.