初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/QwQ-R1-Distill-1.5B-CoT
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- AI-MO/NuminaMath-CoT
+- prithivMLmods/Math-Solve
+- amphora/QwQ-LongCoT-130K
+- prithivMLmods/Deepthink-Reasoning
+- NovaSky-AI/Sky-T1_data_17k
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- QwQ
+- Distill
+- R1
+- Deepseek
+- Qwen2.5
+- text-generation-inference
+---
+
+# **QWQ R1 [Reasoning] Distill 1.5B CoT**
+
+QWQ R1 [Reasoning] Distill 1.5B CoT is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5 R1 Distill from the DeepSeek base model and has been fine-tuned on chain-of-thought (CoT) reasoning datasets, focusing on CoT reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
+
+# **Quickstart with Transformers**
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/QwQ-R1-Distill-1.5B-CoT"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"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**
+
+**QWQ R1 [Reasoning] Distill 1.5B CoT** is specifically designed for tasks requiring advanced reasoning, structured thinking, and detailed explanations. Its intended applications include:
+
+1. **Instruction-Following Tasks**: Performing step-by-step tasks based on user instructions.
+2. **Logical Reasoning**: Solving problems that demand multi-step logical processing and inference.
+3. **Text Generation**: Crafting coherent and contextually appropriate text for various domains.
+4. **Educational Tools**: Assisting in learning environments, providing explanations for complex topics, or guiding through reasoning exercises.
+5. **Problem-Solving**: Addressing computational or real-world problems requiring chain-of-thought reasoning.
+6. **AI-Assisted Decision-Making**: Supporting users in making informed decisions with logical analysis.
+
+# **Limitations**
+
+While the model excels in reasoning and explanation tasks, it has certain constraints:
+
+1. **Context Length**: Limited ability to process or generate outputs for inputs exceeding its maximum token limit.
+2. **Domain Knowledge**: It may lack detailed expertise in niche domains not covered during training.
+3. **Dependence on Training Data**: Performance can be influenced by biases or gaps in the datasets it was fine-tuned on.
+4. **Real-Time Reasoning**: Struggles with tasks requiring dynamic understanding of real-time data or rapidly changing contexts.
+5. **Mathematical Precision**: May produce errors in calculations or fail to interpret ambiguous mathematical problems.
+6. **Factual Accuracy**: Occasionally generates incorrect or outdated information when dealing with facts.
+7. **Language Nuances**: Subtle linguistic or cultural nuances might be misunderstood or misrepresented.
+8. **Complex CoT Chains**: For extremely lengthy or convoluted reasoning chains, the model may lose track of earlier context or steps.