transformers safetensors qwen2 text-generation text-generation-inference conversational license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model:
- prithivMLmods/Cassiopeia-Qwen-14B
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
- Qwen
- Math
- code
---

![asasa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vshs-6BUCkffzJgC5rIG2.png)

# **Gliese-B1257**  

> Gliese-B1257 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-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.  

## **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 = "prithivMLmods/Gliese-B1257"

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 large 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.