Instructions to use jacobpmeyer/book-advisor-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jacobpmeyer/book-advisor-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jacobpmeyer/book-advisor-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jacobpmeyer/book-advisor-merged")
model = AutoModelForCausalLM.from_pretrained("jacobpmeyer/book-advisor-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jacobpmeyer/book-advisor-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jacobpmeyer/book-advisor-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jacobpmeyer/book-advisor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jacobpmeyer/book-advisor-merged

SGLang

How to use jacobpmeyer/book-advisor-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jacobpmeyer/book-advisor-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jacobpmeyer/book-advisor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jacobpmeyer/book-advisor-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jacobpmeyer/book-advisor-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jacobpmeyer/book-advisor-merged with Docker Model Runner:
```
docker model run hf.co/jacobpmeyer/book-advisor-merged
```

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📚 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.

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

F16

Model tree for jacobpmeyer/book-advisor-merged

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2733)

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