Venatici-Coder-14B-Y.2/README.md

---
license: apache-2.0
tags:
- text-generation-inference
- Coder
- RL
- Math
- Y.2
- code
library_name: transformers
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
---

![zgdfrgzdg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/aACsXj377Ko_c7HYW33Cw.png)

# **Venatici-Coder-14B-Y.2**

> **Venatici-Coder-14B-Y.2** is built on the Qwen 2.5 14B modality architecture and enhanced through reinforcement learning to deliver advanced capabilities in coding, computational reasoning, and mathematical problem-solving. This model is fine-tuned for developers and data scientists seeking precision, efficiency, and logical coherence in code generation and explanation tasks.

## **Key Improvements**
1. **Reinforcement-Learned for Coding Excellence**: Fine-tuned via reinforcement learning to optimize structured and context-aware code generation.
2. **Advanced Reasoning Engine**: Tailored to solve complex algorithmic and mathematical problems with step-by-step logic.
3. **Efficient Memory Utilization**: Designed to reduce computational overhead, supporting high-throughput environments.
4. **Extended Context Support**: Accepts up to **128K tokens** of input and can generate **up to 8K tokens** of output, enabling long-form, detailed code and explanations.
5. **Precision-Focused Output**: Reduces noise by limiting unwanted textual tokens, providing clean and actionable code.

## **Quickstart with transformers**

Here is a Python code snippet using `apply_chat_template` to load and generate outputs from the model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Venatici-Coder-14B-Y.2"

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

prompt = "Write a Python function to find the Fibonacci sequence."
messages = [
    {"role": "system", "content": "You are an advanced reasoning-based coding assistant."},
    {"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**
1. **Code Generation & Refactoring**  
   Designed to help write, debug, and optimize code across diverse programming languages.

2. **Algorithm Design & Math Problem Solving**  
   Excels in structured logical reasoning, computational tasks, and math-heavy scenarios.

3. **Technical Explanation & Learning Aid**  
   Breaks down complex coding topics, making it ideal for learning and teaching.

4. **Debugging & Troubleshooting**  
   Identifies errors, suggests corrections, and explains root causes.

5. **Structured Data Workflows**  
   Generates and parses structured data formats (JSON, XML, CSV) for data pipelines and API development.

## **Limitations**
1. **Hardware Intensive**  
   Requires high-memory GPU/TPU setups due to its parameter size and extended token limits.

2. **Bias Reflection**  
   May exhibit biases present in the training data, despite reinforcement tuning.

3. **Creative Variability**  
   Not ideal for creative writing or narrative generation.

4. **No Real-Time Awareness**  
   Responses are based on pre-trained knowledge without awareness of recent events.

5. **Error Propagation in Long Outputs**  
   Minor errors can cascade in extended generations.

6. **Prompt Sensitivity**  
   Output quality can depend on how clearly the input is phrased.