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Model: jinaai/reader-lm-0.5b
Source: Original Platform

.gitattributes

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README.md

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+---
+pipeline_tag: text-generation
+language:
+- multilingual
+inference: false
+license: cc-by-nc-4.0
+library_name: transformers
+---
+
+<br><br>
+
+<p align="center">
+<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
+</p>
+
+<p align="center">
+<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+
+[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)
+
+
+# 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](https://huggingface.co/jinaai/reader-lm-0.5b)       |
+| reader-lm-1.5b  |  256K             | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-1.5b)       |
+|                 |
+
+
+# Quick Start
+
+## On Google Colab
+The easiest way to experience reader-lm is by running [our Colab notebook](https://colab.research.google.com/drive/1wXWyj5hOxEHY6WeHbOwEzYAC0WB1I5uA), 
+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.
+
+
+
+## Local
+To use this model, you need to install `transformers`:
+
+
+
+```bash
+pip install transformers<=4.43.4
+```
+
+Then, you can use the model as follows:
+
+```python
+# pip install transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "jinaai/reader-lm-0.5b"
+
+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]))
+```
+
+## AWS Sagemaker & Azure Marketplace
+[AWS 0.5b](https://aws.amazon.com/marketplace/pp/prodview-nli7b6dueo424?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)
+[AWS 1.5b](https://aws.amazon.com/marketplace/pp/prodview-ms27ixcwq3wjk?sr=0-2&ref_=beagle&applicationId=AWSMPContessa)
+[Azure 0.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-500m)
+[Azure 1.5b](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.reader-lm-1500m)

added_tokens.json

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+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config.json

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+{
+  "_name_or_path": "jinaai/qwen2-0.5b-reader",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 256000,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 2000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{}

generation_config.json

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+  "bos_token_id": 151643,
+  "do_sample": true,
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+  "repetition_penalty": 1.1,
+  "rope_theta": 2000000,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.43.3",
+  "use_cache": false
+}

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special_tokens_map.json

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+    "<|im_start|>",
+    "<|im_end|>"
+  ],
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+    "single_word": false
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tokenizer_config.json

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+{
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+  "added_tokens_decoder": {
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+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": null,
+  "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 %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
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+}