---
library_name: transformers
tags:
- text-generation-inference
- reinforcement-learning
- code
- math
- moe
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Qwen3-4B-ft-bf16
pipeline_tag: text-generation
---

![56.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/M3O20DjhtBBnGoD-y2meN.png)

# **BetaCeti-Beta-4B-Prime1**

> **BetaCeti-Beta-4B-Prime1** is a compact, coding-optimized language model built on the **Qwen3-4B architecture**, tailored for high-accuracy **code generation**, **debugging**, and **technical reasoning**. With **4 billion parameters**, it strikes a balance between performance and efficiency, making it an ideal assistant for developers, educators, and engineers working in constrained environments or requiring fast inference.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/BetaCeti-Beta-4B-Prime1-GGUF](https://huggingface.co/prithivMLmods/BetaCeti-Beta-4B-Prime1-GGUF)

---

## **Key Features**

1. **Qwen3-4B Architecture Core**
   Built on the robust and scalable **Qwen3** transformer backbone, offering strong performance on both single-turn and multi-step code workflows.

2. **Code-First Training Focus**
   Fine-tuned primarily on coding datasets across Python, JavaScript, C++, and Bash, with additional coverage of software documentation, APIs, and debugging tasks.

3. **Multi-Step Reasoning in Code**
   Capable of breaking down complex programming problems, explaining logic, and correcting bugs—ideal for students, engineers, and software instructors.

4. **Structured Format Proficiency**
   Outputs syntactically correct code blocks, JSON, YAML, and Markdown—streamlining integration into tools, notebooks, and docs.

5. **Lightweight Yet Powerful**
   At 4B parameters, it provides strong results without the heavy resource demands of larger models, and is deployable on most modern GPUs or powerful CPUs.

6. **Cross-Language Coding Support**
   Generates and interprets code in 10+ languages with emphasis on real-world application, scripting, and algorithmic problem-solving.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/BetaCeti-Beta-4B-Prime1"

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

prompt = "Write a Python function to check if a number is prime."

messages = [
    {"role": "system", "content": "You are a helpful 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**

* Code generation, translation, and refactoring
* Teaching and tutoring in programming concepts
* Technical documentation generation and API auto-fill
* Debugging assistant with error analysis and fixes
* Lightweight deployment in IDEs, coding platforms, and offline environments

---

## **Limitations**

* Smaller context length compared to larger coding models (e.g., >7B)
* May require prompt engineering for deeply nested or obscure code patterns
* Limited fluency in non-programming natural language dialogue
* Not optimized for purely creative writing or storytelling tasks

---

## **References**

1. \[Qwen2.5 Technical Report (https://arxiv.org/pdf/2412.15115)]
2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)