---
library_name: transformers
tags:
- text-generation-inference
- pretraining/SFT
- code
- math
license: apache-2.0
language:
- en
base_model:
- Gensyn/Qwen2.5-0.5B-Instruct
pipeline_tag: text-generation
---
 
![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/O97CXaIMZRnhzV7yZhZV4.png)

# **Lang-Exster-0.5B-Instruct**

> **Lang-Exster-0.5B-Instruct** is a **general-purpose instruction-following LLM** fine-tuned from **Qwen2.5-0.5B**. This model is optimized for **lightweight deployments** and **instructional clarity**, capable of performing a wide range of natural language and programming-related tasks with efficiency and interpretability.

## **Key Features**

1. **Instruction Following & Explanation**  
   Trained to **understand, follow, and respond** to natural language instructions with clear, logical, and relevant output. Suitable for Q&A, step-by-step reasoning, and guided code generation.

2. **Lightweight General-Purpose Model**  
   Fine-tuned from **Qwen2.5-0.5B**, making it **highly efficient for edge devices**, **local tools**, and **low-resource applications** without sacrificing utility.

3. **Multi-Domain Task Handling**  
   Can perform across **coding**, **writing**, **summarization**, **chat**, **translation**, and **educational queries**, thanks to its broad general-purpose instruction tuning.

4. **Compact and Efficient**  
   At just **0.5B parameters**, Lang-Exster is optimized for **fast inference**, **low memory usage**, and seamless integration into developer tools and workflows.

5. **Code Assistance (Lite)**  
   Capable of **basic code generation**, **syntax checking**, and **conceptual explanations**, especially useful for beginners and instructional applications.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Lang-Exster-0.5B-Instruct"

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

prompt = "Write a Python function that checks if a number is prime, and explain how it works."

messages = [
    {"role": "system", "content": "You are an instructional assistant. Follow user instructions clearly and explain your reasoning."},
    {"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**

- **General-Purpose Assistant**:  
  Performs everyday tasks such as Q&A, summarization, light coding, language generation, and translation.

- **Educational Support**:  
  Aids learners in understanding topics through **guided explanations**, **basic coding help**, and **concept breakdowns**.

- **Lightweight Developer Integration**:  
  Ideal for command-line assistants, browser plugins, and desktop utilities with limited compute resources.

- **Instruction Clarity Demonstrator**:  
  Acts as a fine baseline for developing **instruction-tuned** capabilities in constrained environments.

## **Limitations**

1. **Scale Limitations**  
   Being a 0.5B model, it has limited memory and may not handle deep context or long documents effectively.

2. **Reasoning Depth**  
   Provides **surface-level reasoning** and may struggle with highly technical, abstract, or creative prompts.

3. **Basic Code Generation**  
   Supports basic scripting and logic but may miss edge cases or advanced patterns in complex code.

4. **Prompt Design Sensitivity**  
   Performs best with **clear**, **concise**, and **well-structured** instructions.