QuantFactory/reader-lm-1.5b-GGUF

This is quantized version of jinaai/reader-lm-1.5b created using llama.cpp

Original Model Card

Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications.

Trained by Jina AI.

Intro

Jina Reader-LM is a series of models that convert HTML content to Markdown content, which is useful for content conversion tasks. The model is trained on a curated collection of HTML content and its corresponding Markdown content.

Models

Name	Context Length	Download
reader-lm-0.5b	256K	🤗 Hugging Face
reader-lm-1.5b	256K	🤗 Hugging Face

Evaluation

TBD

Quick Start

To use this model, you need to install transformers:

pip install transformers<=4.43.4

Then, you can use the model as follows:

# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "jinaai/reader-lm-1.5b"

device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

# example html content
html_content = "<html><body><h1>Hello, world!</h1></body></html>"

messages = [{"role": "user", "content": html_content}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)

print(input_text)

inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)

print(tokenizer.decode(outputs[0]))