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Model: prithivMLmods/QwQ-LCoT-7B-Instruct Source: Original Platform
2026-06-03 18:32:15 +08:00
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+---
+license: creativeml-openrail-m
+datasets:
+- amphora/QwQ-LongCoT-130K
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Long-CoT
+- Qwen2.5
+- 7B
+- safetensors
+- text-generation-inference
+- QwQ
+- SFT
+- Math
+- Qwen with Questions
+new_version: prithivMLmods/QwQ-LCoT2-7B-Instruct
+---
+
+# **QwQ-LCoT-7B-Instruct Model File**
+
+The QwQ-LCoT-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 amphora/QwQ-LongCoT-130K dataset, focusing on chain-of-thought (CoT) reasoning. 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-LCoT-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]
+```
+
+### **Sample Long CoT:**
+
+![Screenshot 2024-12-13 211732.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Mgm9LmQZlFZmglKYwEDYA.png)
+
+---
+### **Key Features:**
+
+1. **Model Size:**  
+   - **7.62B parameters** (FP16 precision).  
+
+2. **Model Sharding:**  
+   - The model weights are split into 4 shards (`safetensors`) for efficient storage and download:
+     - `model-00001-of-00004.safetensors` (4.88 GB)
+     - `model-00002-of-00004.safetensors` (4.93 GB)
+     - `model-00003-of-00004.safetensors` (4.33 GB)
+     - `model-00004-of-00004.safetensors` (1.09 GB)
+
+3. **Tokenizer:**  
+   - Byte-pair encoding (BPE) based.
+   - Files included:
+     - `vocab.json` (2.78 MB)
+     - `merges.txt` (1.82 MB)
+     - `tokenizer.json` (11.4 MB)
+   - Special tokens mapped in `special_tokens_map.json` (e.g., `<pad>`, `<eos>`).
+
+4. **Configuration Files:**  
+   - `config.json`: Defines model architecture and hyperparameters.
+   - `generation_config.json`: Settings for inference and text generation tasks.
+
+---
+
+### **Training Dataset:**  
+- **Dataset Name:** [amphora/QwQ-LongCoT-130K](https://huggingface.co/datasets/amphora/QwQ-LongCoT-130K)  
+- **Size:** 133k examples.  
+- **Focus:** Chain-of-Thought reasoning for complex tasks.
+
+---
+
+### **Use Cases:**
+1. **Instruction Following:**  
+   Handle user instructions effectively, even for multi-step tasks.
+   
+2. **Reasoning Tasks:**  
+   Perform logical reasoning and generate detailed step-by-step solutions.
+   
+3. **Text Generation:**  
+   Generate coherent, context-aware responses.
+---