---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/QwQ-LCoT2-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- sqweeks
- reasoner
- qwen
- 7b
---
# **Sqweeks-7B-Instruct**  

Sqweeks-7B-Instruct is based on the Qwen 2.5 7B modality architecture, designed to enhance the reasoning capabilities of 7B-parameter models. It has been fine-tuned on a synthetic dataset based on open-thoughts & general corpus reasoning entries, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.  

### **Key Improvements**  
1. **Enhanced Knowledge and Expertise**: Improved mathematical reasoning, coding proficiency, and structured data processing.  
2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).  
3. **Greater Adaptability**: Better role-playing capabilities and resilience to diverse system prompts.  
4. **Long-Context Support**: Handles up to **64K tokens** and generates up to **4K tokens** per output.  
5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.  

### **Quickstart with Transformers**  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Sqweeks-7B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
    {"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]
print(response)
```

### **Intended Use**  
- **Advanced Reasoning & Context Understanding**: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.  
- **Mathematical & Scientific Problem-Solving**: Enhanced capabilities for calculations, theorem proving, and scientific queries.  
- **Code Generation & Debugging**: Generates and optimizes code across multiple programming languages.  
- **Structured Data Analysis**: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.  
- **Multilingual Applications**: High proficiency in over 29 languages, enabling global-scale applications.  
- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.  

### **Limitations**  
1. **Computational Requirements**: Despite being a **7B-parameter** model, it still requires a capable GPU for efficient inference.  
2. **Language-Specific Variability**: Performance may vary across supported languages, especially for low-resource languages.  
3. **Potential Error Accumulation**: Long-text generation can sometimes introduce inconsistencies over extended outputs.  
4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.  
5. **Prompt Sensitivity**: Outputs can depend on the specificity and clarity of the input prompt.