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README.md
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---
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pipeline_tag: text-generation
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language:
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- multilingual
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inference: false
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license: cc-by-nc-4.0
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library_name: transformers
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/reader-lm-1.5b-GGUF
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This is quantized version of [jinaai/reader-lm-1.5b](https://huggingface.co/jinaai/reader-lm-1.5b) created using llama.cpp
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# Original Model Card
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<br><br>
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<p align="center">
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<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">
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</p>
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<p align="center">
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<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
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</p>
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# Intro
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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.
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# Models
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| Name | Context Length | Download |
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|-----------------|-------------------|-----------------------------------------------------------------------|
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| reader-lm-0.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-0.5b) |
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| reader-lm-1.5b | 256K | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-1.5b) |
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# Evaluation
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TBD
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# Quick Start
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To use this model, you need to install `transformers`:
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```bash
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pip install transformers<=4.43.4
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```
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Then, you can use the model as follows:
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```python
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# pip install transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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checkpoint = "jinaai/reader-lm-1.5b"
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device = "cuda" # for GPU usage or "cpu" for CPU usage
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
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# example html content
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html_content = "<html><body><h1>Hello, world!</h1></body></html>"
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messages = [{"role": "user", "content": html_content}]
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input_text=tokenizer.apply_chat_template(messages, tokenize=False)
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print(input_text)
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)
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print(tokenizer.decode(outputs[0]))
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```
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