QwQ-LCoT2-7B-Instruct

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 of thought reasoning datasets, focusing on chain-of-thought (CoT) reasoning for problems. 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.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"

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

The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:

1. Instruction Following: Providing detailed and step-by-step guidance for a wide range of user queries.
2. Logical Reasoning: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
3. Text Generation: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
4. Problem-Solving: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support.
5. Knowledge Enhancement: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.

Limitations

1. Data Bias: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
2. Context Limitation: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context.
3. Complexity Ceiling: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs.
4. Dependency on Prompt Quality: The quality and specificity of the user prompt heavily influence the model's responses.
5. Non-Factual Outputs: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics.
6. Computational Requirements: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.