README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- LCoT
- Qwen
- v2
datasets:
- PowerInfer/QWQ-LONGCOT-500K
- AI-MO/NuminaMath-CoT
- prithivMLmods/Math-Solve
- amphora/QwQ-LongCoT-130K
- prithivMLmods/Deepthink-Reasoning
model-index:
- name: QwQ-LCoT2-7B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 55.76
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 34.37
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 22.21
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.38
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 15.75
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
---



# **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.

```python
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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__QwQ-LCoT2-7B-Instruct-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FQwQ-LCoT2-7B-Instruct&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    28.60|
|IFEval (0-Shot)    |    55.76|
|BBH (3-Shot)       |    34.37|
|MATH Lvl 5 (4-Shot)|    22.21|
|GPQA (0-shot)      |     6.38|
|MuSR (0-shot)      |    15.75|
|MMLU-PRO (5-shot)  |    37.13|