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README.md
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README.md
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# reader-lm-1.5b_a13683982864871424733474
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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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<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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[Blog](https://jina.ai/news/reader-lm-small-language-models-for-cleaning-and-converting-html-to-markdown) | [Colab](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA)
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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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# Get Started
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## On Google Colab
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The easiest way to experience reader-lm is by running [our Colab notebook](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA),
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where we demonstrate how to use reader-lm-1.5b to convert the HackerNews website into markdown. The notebook is optimized to run smoothly on Google Colab’s free T4 GPU tier. You can also load reader-lm-0.5b or change the URL to any website and explore the output. Note that the input (i.e., the prompt) to the model is the raw HTML—no prefix instruction is required.
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## Local
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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 modelscope
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from modelscope 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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## AWS Sagemaker & Azure Marketplace
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[AWS 0.5b](https://aws.amazon.com/marketplace/pp/prodview-nli7b6dueo424?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)
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[AWS 1.5b](https://aws.amazon.com/marketplace/pp/prodview-ms27ixcwq3wjk?sr=0-2&ref_=beagle&applicationId=AWSMPContessa)
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[Azure 0.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-500m)
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[Azure 1.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-1500m)
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reader-lm-1.5b
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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{
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"_name_or_path": "jinaai/qwen2-1.5b-reader",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 256000,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 2000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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{}
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{
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"attn_implementation": "flash_attention_2",
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"rope_theta": 2000000,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.43.3",
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"use_cache": false
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}
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"151643": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151644": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151645": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": null,
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"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"model_max_length": 256000,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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