|
|
--- |
|
|
license: apache-2.0 |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- Qwen/Qwen2.5-14B-Instruct-1M |
|
|
pipeline_tag: text-generation |
|
|
library_name: transformers |
|
|
tags: |
|
|
- text-generation-inference |
|
|
- code |
|
|
- Qwen |
|
|
- 14B |
|
|
- QWQ |
|
|
- Math |
|
|
- trl |
|
|
--- |
|
|
|
|
|
 |
|
|
|
|
|
# **Cassiopeia-Qwen-14B** |
|
|
|
|
|
Cassiopeia-Qwen-14B 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/Cassiopeia-Qwen-14B" |
|
|
|
|
|
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. |
