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Model: prithivMLmods/Phi-4-QwQ
Source: Original Platform

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README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- microsoft/phi-4
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- llama
+- phi3
+- phi
+---
+![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UV-0cgT9xB7-l0GZIiZv7.png)
+
+# **Phi-4-QwQ [ Responsible Problem Solving & Advanced Reasoning ]**
+
+`[Phi-4-QwQ finetuned]` from Microsoft's Phi-4 is a state-of-the-art open model developed with a focus on **responsible problem solving** and **advanced reasoning capabilities**. Built upon a diverse blend of synthetic datasets, carefully filtered public domain websites, and high-quality academic books and Q&A datasets, Phi-4-QwQ ensures that small, capable models are trained with datasets of exceptional depth and precision.
+
+Phi-4-QwQ adopts a robust **safety post-training approach** using open-source and in-house synthetic datasets. This involves a combination of **SFT (Supervised Fine-Tuning)** and iterative **DPO (Direct Preference Optimization)** techniques, ensuring helpful and harmless outputs across various safety categories.
+
+---
+
+# **Dataset Info**
+
+Phi-4-QwQ is fine-tuned on a carefully curated synthetic dataset generated using an advanced pipeline optimized for **Chain of Thought (CoT)** reasoning and **Responsible Problem Breakdown (RPB)** methodologies. This ensures that the model excels at:
+
+- **Logical reasoning**  
+- **Step-by-step problem-solving**  
+- **Breaking down complex tasks into manageable parts**  
+
+The dataset also emphasizes responsible decision-making and fairness in generating solutions.
+
+---
+
+# **Run with Transformers**
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-QwQ")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/Phi-4-QwQ",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+input_text = "Explain the concept of black holes."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+```
+
+For chat-style interactions, use `tokenizer.apply_chat_template`:
+
+```python
+messages = [
+    {"role": "user", "content": "Explain the concept of black holes."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+```
+
+# **Intended Use**
+
+Phi-4-QwQ is tailored for a wide range of applications, especially those involving **advanced reasoning**, **multilingual capabilities**, and **responsible problem-solving**. Its primary use cases include:
+
+1. **Responsible Problem Solving**  
+   - Breaking down complex problems into logical, actionable steps.  
+   - Offering ethical, well-rounded solutions in academic and professional contexts.  
+
+2. **Advanced Reasoning Tasks**  
+   - Excelling in mathematics, logic, and scientific reasoning.  
+   - Providing detailed explanations and systematic answers.  
+
+3. **Content Generation**  
+   - Assisting in generating high-quality content for various domains, including creative writing and technical documentation.  
+   - Supporting marketers, writers, and educators with detailed and well-structured outputs.  
+
+4. **Educational Support**  
+   - Acting as a virtual tutor for students by generating practice questions, answers, and detailed explanations.  
+   - Helping educators design learning material that promotes critical thinking and step-by-step problem-solving.  
+
+5. **Customer Support & Dialogue Systems**  
+   - Enabling chatbots and virtual assistants to provide accurate, helpful, and responsible responses.  
+   - Enhancing customer service with reasoning-driven automation.  
+
+6. **Multilingual Capabilities**  
+   - Supporting multilingual communication and content generation while maintaining contextual accuracy.  
+   - Assisting in translations with a focus on retaining meaning and nuance.
+
+7. **Safety-Critical Applications**  
+   - Ensuring safe and harmless outputs, making it suitable for sensitive domains.  
+   - Providing aligned interactions with human oversight for critical systems.
+
+---
+
+# **Limitations**
+
+Despite its strengths, Phi-4-QwQ has some limitations that users should be aware of:
+
+1. **Bias and Fairness**  
+   - While great effort has been made to minimize biases, users should critically assess the model’s output in sensitive scenarios to avoid unintended bias.  
+
+2. **Contextual Interpretation**  
+   - The model may occasionally misinterpret highly nuanced prompts or ambiguous contexts, leading to suboptimal responses.  
+
+3. **Knowledge Cutoff**  
+   - Phi-4-QwQ’s knowledge is static and based on the data available at the time of training. It does not include real-time updates or information on recent developments.  
+
+4. **Safety and Harmlessness**  
+   - Despite post-training safety alignment, inappropriate or harmful outputs may still occur. Continuous monitoring and human oversight are advised when using the model in critical contexts.  
+
+5. **Computational Requirements**  
+   - Deploying Phi-4-QwQ efficiently may require substantial computational resources, particularly for large-scale deployments or real-time applications.  
+
+6. **Ethical Considerations**  
+   - Users are responsible for ensuring that the model is not employed for malicious purposes, such as spreading misinformation, generating harmful content, or facilitating unethical behavior.  
+
+7. **Domain-Specific Expertise**  
+   - While the model is versatile, it may not perform optimally in highly specialized domains (e.g., law, medicine, finance) without further domain-specific fine-tuning.

config.json

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+{
+  "_name_or_path": "microsoft/phi-4",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 100257,
+  "eos_token_id": 100265,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17920,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 10,
+  "original_max_position_embeddings": 16384,
+  "pad_token_id": 100351,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 250000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "vocab_size": 100352
+}

configuration.json

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

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 100257,
+  "eos_token_id": 100265,
+  "max_length": 16384,
+  "pad_token_id": 100351,
+  "transformers_version": "4.47.1"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
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+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
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+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
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+    "content": "�",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,791 @@
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+  "added_tokens_decoder": {
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+    },
+    "100343": {
+      "content": "<|dummy_79|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100344": {
+      "content": "<|dummy_80|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100345": {
+      "content": "<|dummy_81|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100346": {
+      "content": "<|dummy_82|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100347": {
+      "content": "<|dummy_83|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100348": {
+      "content": "<|dummy_84|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100349": {
+      "content": "<|dummy_85|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100350": {
+      "content": "<|dummy_86|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "100351": {
+      "content": "<|dummy_87|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": {},
+  "model_max_length": 16384,
+  "pad_token": "<|dummy_87|>",
+  "padding_side": "left",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "�"
+}