prithivMLmods/QwQ-LCoT-3B-Instruct

QwQ-LCoT-3B-Instruct Model Card

The QwQ-LCoT-3B-Instruct model is a lightweight, instruction-tuned language model designed for complex reasoning and explanation tasks. It is fine-tuned on the Qwen2.5-3B-Instruct base model using the QwQ-LongCoT-130K dataset, focusing on long-chain-of-thought (LCoT) reasoning for enhanced logical comprehension and detailed output generation.

File Name	Size	Description	Upload Status
.gitattributes	1.57 kB	Specifies LFS tracking for large files.	Uploaded
README.md	267 Bytes	Basic project information file.	Updated
added_tokens.json	657 Bytes	Custom tokens added to the tokenizer.	Uploaded
config.json	859 Bytes	Configuration file for the model.	Uploaded
generation_config.json	281 Bytes	Configuration file for text generation settings.	Uploaded
merges.txt	1.82 MB	Contains the byte-pair encoding (BPE) merges.	Uploaded
pytorch_model-00001-of-00002.bin	4.96 GB	First shard of the model weights in PyTorch format.	Uploaded (LFS)
pytorch_model-00002-of-00002.bin	1.21 GB	Second shard of the model weights in PyTorch format.	Uploaded (LFS)
pytorch_model.bin.index.json	36 kB	Index mapping for sharded model weights.	Uploaded
special_tokens_map.json	644 Bytes	Maps special tokens to their roles.	Uploaded
tokenizer.json	11.4 MB	Serialized tokenizer data.	Uploaded (LFS)
tokenizer_config.json	7.73 kB	Tokenizer configuration settings.	Uploaded
vocab.json	2.78 MB	Vocabulary file for the tokenizer.	Uploaded

Sample Long CoT:

Key Features:

1. Long Chain-of-Thought Reasoning:

   • Specifically designed to generate comprehensive, step-by-step explanations for complex queries.

2. Lightweight and Efficient:

   • With only 3 billion parameters, it is optimized for systems with limited computational resources without compromising reasoning capabilities.

3. Instruction Optimization:

   • Fine-tuned to follow prompts and provide concise, actionable, and structured responses.

---

Training Details:

• Base Model: Qwen2.5-3B-Instruct
• Dataset: amphora/QwQ-LongCoT-130K
  • Comprising 133,000 annotated samples focusing on logical tasks and structured thinking.

---

Capabilities:

1. Text Generation:

   • Provides detailed, structured, and logical text outputs tailored to user prompts.

2. Reasoning Tasks:

   • Solves step-by-step problems in math, logic, and science.

3. Educational Assistance:

   • Generates coherent explanations for academic and research purposes.

4. Dialogue and Summarization:

   • Handles conversational queries and summarizes long documents effectively.

---

Usage Instructions:

1. Setup: Download all model files and ensure compatibility with the Hugging Face Transformers library.

2. Loading the Model:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "prithivMLmods/QwQ-LCoT-3B-Instruct"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)
   

3. Generate Long-Chain Reasoning Outputs:

   input_text = "Explain the process of photosynthesis step-by-step."
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs, max_length=300, temperature=0.5)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Customize Output Generation:
   Modify the generation_config.json file for different scenarios:

   • temperature: Controls randomness (lower = deterministic, higher = creative).
   • max_length: Sets response length.
   • top_p: Adjusts sampling for diversity in outputs.

---