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reader-lm-1.5b-GGUF
QuantFactory/reader-lm-1.5b-GGUF
reader-lm-1.5b-GGUF is a text generation model from QuantFactory. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
All-time downloads
5.5K
Public
Repo size
16.5 GB
Likes
4
Public
Repo makeup
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.gguf16.5 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Sep 12, 2024
- Updated
- Sep 12, 2024
- SHA
- 1fc9d915
Model card
From the Hugging Face model README
QuantFactory/reader-lm-1.5b-GGUF
This is quantized version of jinaai/reader-lm-1.5b created using llama.cpp
Original Model Card
<br><br>
<p align="center"> <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="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." width="150px"> </p> <p align="center"> <b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p>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]))
- Task
- Text Generation
- Library
- transformers
- License
- cc-by-nc-4.0
- Languages
- multilingual
- Created
- Sep 12, 2024
- Updated
- Sep 12, 2024