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Model: prithivMLmods/Raptor-X2
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+datasets:
+- smirki/UI_REASONING_v1.01
+- SynthLabsAI/Big-Math-RL-Verified
+- open-r1/OpenR1-Math-220k
+- HuggingFaceH4/MATH-500
+language:
+- en
+base_model:
+- prithivMLmods/Viper-Coder-v1.1
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- Non-Reasoning
+- Raptor
+- Coder
+- X2
+- Html
+- Css
+- React
+- Python
+- Java
+- Qwen
+- Math
+---
+![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KgTS_ikorbO6TlU6qoNpS.png)
+
+# **Raptor-X2**
+
+> [!warning]
+> Non-Reasoning
+
+> Raptor-X2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced mathematical explanations, scientific reasoning, and general-purpose coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor-X2 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
+
+Key improvements include:  
+1. **Enhanced Mathematical Reasoning**: Provides step-by-step explanations for complex mathematical problems, making it useful for students, researchers, and professionals.
+2. **Advanced Scientific Understanding**: Excels in explaining scientific concepts across physics, chemistry, biology, and engineering.
+3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation.
+4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
+5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+# **Quickstart with transformers**
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Raptor-X2"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the fundamental theorem of calculus."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+# **Intended Use**  
+1. **Mathematical Explanation**:  
+   Designed for providing step-by-step solutions to mathematical problems, including algebra, calculus, and discrete mathematics.  
+2. **Scientific Reasoning**:  
+   Suitable for explaining scientific theories, conducting physics simulations, and solving chemistry equations.  
+3. **Programming and Software Development**:  
+   Capable of generating, analyzing, and optimizing code in multiple programming languages.  
+4. **Educational Assistance**:  
+   Helps students and researchers by providing explanations, summaries, and structured learning material.  
+5. **Multilingual Applications**:  
+   Supports global communication, translations, and multilingual content generation.  
+6. **Long-Form Content Generation**:  
+   Can generate extended responses, including research papers, documentation, and technical reports.  
+
+# **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+2. **Potential Bias in Responses**:  
+   While designed to be neutral, outputs may still reflect biases present in training data.  
+3. **Complexity in Some Scientific Domains**:  
+   While proficient in general science, highly specialized fields may require verification.  
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time events beyond its training cutoff.  
+5. **Error Propagation in Extended Outputs**:  
+   Minor errors in early responses may affect overall coherence in long-form outputs.  
+6. **Prompt Sensitivity**:  
+   The effectiveness of responses may depend on how well the input prompt is structured.

added_tokens.json

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config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 120000,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
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+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
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+    "rope_type": "linear",
+    "type": "linear"
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+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  "max_length": 120000,
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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.49.0"
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special_tokens_map.json

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tokenizer_config.json

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 120000,
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+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}