Pocket-Llama2-3.2-3B-Instruct/README.md

---
library_name: transformers
tags:
- trl
- sft
- text-generation-inference
- code
- Math
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
---

![fdgbxfdgbxfdg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WshE9-ujZLKviO69eSzCv.png)

# **Pocket-Llama2-3.2-3B-Instruct**  

> Pocket-Llama2-3.2-3B-Instruct is based on the Llama 3.2 architecture, designed as a lightweight and efficient general-purpose chat assistant. Optimized for fast inference while maintaining strong problem-solving, mathematical reasoning, and scientific capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.

## **Key Improvements**  
1. **Optimized for General Purpose Chat**: Excels in a wide range of topics, including casual conversation, technical discussions, and knowledge-based queries.  
2. **Strong Math & Science Capabilities**: Provides accurate and structured explanations for mathematical and scientific problems.  
3. **Compact yet Powerful**: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.  
4. **Advanced Reasoning Capabilities**: Excels in algorithmic problem-solving, structured technical explanations, and logical analysis.  
5. **Efficient Memory Utilization**: Reduces computational overhead while maintaining high-quality outputs.  
6. **Focused Output Generation**: Avoids unnecessary token generation, ensuring concise and relevant responses.  

## **Quickstart with transformers**  

Here is a code snippet to load the tokenizer and model using `apply_chat_template` for structured input formatting:  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Pocket-Llama2-3.2-3B-Instruct"

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

prompt = "Explain the theory of relativity in simple terms."
messages = [
    {"role": "system", "content": "You are an advanced assistant specialized in science and mathematics."},
    {"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=6090
)
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**  
1. **General Chat & Knowledge-Based Queries**:  
   Engages in informative and casual discussions on a wide range of topics.  
2. **Mathematics & Science Problem Solving**:  
   Provides accurate calculations and structured explanations for complex problems.  
3. **Technical Documentation & Explanation**:  
   Assists in generating well-structured documentation for APIs, scientific concepts, and coding principles.  
4. **Debugging Assistance**:  
   Helps identify and correct errors in code snippets.  
5. **Educational Support**:  
   Simplifies complex topics for students and learners with clear explanations.  
6. **Structured Data Processing**:  
   Generates structured outputs like JSON, XML, and tables for data science applications.  

## **Limitations**  
1. **Hardware Constraints**:  
   Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.  
2. **Potential Bias in Responses**:  
   Outputs may reflect biases present in training data.  
3. **Limited Creativity**:  
   May generate variable results in non-technical, creative tasks.  
4. **No Real-Time Awareness**:  
   Lacks access to real-world events beyond its training cutoff.  
5. **Error Propagation in Long Responses**:  
   Minor mistakes in early outputs may affect overall coherence in lengthy responses.  
6. **Prompt Sensitivity**:  
   The effectiveness of responses depends on well-structured prompts.