README.md

---
license: apache-2.0
language:
- en
library_name: transformers
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
tags:
- text-generation-inference
- math
- code
- qwen
- biology
- prime2
- reinforcement-learning
- trl
---
 
![45.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/8l-b6UVyhAh9nQEFyrJmp.png)

# **Omega-Herculis-7B-Prime2**

> Omega-Herculis-7B-Prime2 is based on the Qwen 2.5 7B architecture, designed to enhance the reasoning capabilities of 7B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.

---

## **Key Improvements**

1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.
2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.
3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.
4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

---

## **Quickstart with transformers**

Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-namespace/Omega-Herculis-7B-Prime2"  # Replace with actual model path

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

prompt = "What are the key principles of general-purpose AI?"
messages = [
    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
    {"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**

1. **General-Purpose Reasoning**
   Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.

2. **Educational and Informational Assistance**
   Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.

3. **Conversational AI and Chatbots**
   Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.

4. **Multilingual Applications**
   Supports global communication, translations, and multilingual content generation.

5. **Structured Data Processing**
   Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.

6. **Long-Form Content Generation**
   Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.

---

## **Limitations**

1. **Hardware Requirements**
   Requires high-memory GPUs or TPUs due to its parameter size and long-context support.

2. **Potential Bias in Responses**
   While designed to be neutral, outputs may still reflect biases present in training data.

3. **Inconsistent Outputs in Creative Tasks**
   May produce variable results in storytelling and highly subjective topics.

4. **Limited Real-World Awareness**
   Does not have access to real-time events beyond its training cutoff.

5. **Error Propagation in Extended Outputs**
   Minor errors in early responses may affect overall coherence in long-form outputs.

6. **Prompt Sensitivity**
   The effectiveness of responses may depend on how well the input prompt is structured.