Triangle104
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QwQ-LCoT2-7B-Instruct-Q4_K_S-GGUF

@@ -124,6 +124,7 @@ Refer to the [original model card](https://huggingface.co/prithivMLmods/QwQ-LCoT
 ---
 Model details:
 The QwQ-LCoT2-7B-Instruct is a fine-tuned language model
 designed for advanced reasoning and instruction-following tasks. It
 leverages the Qwen2.5-7B base model and has been fine-tuned on the chain
@@ -133,20 +134,10 @@ 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.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"
@@ -180,23 +171,11 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 		Intended Use
 The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning
 and instruction-following tasks, with specific applications including:
 Instruction Following: Providing detailed and step-by-step guidance for a wide range of user queries.
 Logical Reasoning: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
 Text Generation: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
@@ -205,17 +184,8 @@ that require chain-of-thought (CoT) reasoning, making it ideal for
 education, tutoring, and technical support.
 Knowledge Enhancement: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
 		Limitations
 Data Bias: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
 Context Limitation: Performance may degrade for
 tasks requiring knowledge or reasoning that significantly exceeds the

 ---
 Model details:
+-
 The QwQ-LCoT2-7B-Instruct is a fine-tuned language model
 designed for advanced reasoning and instruction-following tasks. It
 leverages the Qwen2.5-7B base model and has been fine-tuned on the chain
 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.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 		Intended Use
 The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning
 and instruction-following tasks, with specific applications including:
 Instruction Following: Providing detailed and step-by-step guidance for a wide range of user queries.
 Logical Reasoning: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
 Text Generation: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
 education, tutoring, and technical support.
 Knowledge Enhancement: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
 		Limitations
 Data Bias: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
 Context Limitation: Performance may degrade for
 tasks requiring knowledge or reasoning that significantly exceeds the