---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/Jolt-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- Dinobot
- Exp
model-index:
- name: Dinobot-Opus-14B-Exp
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 82.4
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 48.2
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 53.17
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.96
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 12.65
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 44.21
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FDinobot-Opus-14B-Exp
      name: Open LLM Leaderboard
---
![zsdfgvsd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eZlME6bkADDIdN8hf0_JM.png)
# **Dinobot-Opus-14B-Exp**  

Dinobot-Opus-14B-Exp [abliterated] is a high-performance, general-purpose language model based on the Qwen 2.5 14B architecture. It is designed to excel in reasoning, explanation, and conversational tasks, offering enhanced capabilities in contextual understanding, logical deduction, and multi-step problem-solving. This model has been fine-tuned using advanced chain-of-thought reasoning techniques 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 its ability to answer questions accurately and generate 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 = "prithivMLmods/Dinobot-Opus-14B-Exp"

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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Dinobot-Opus-14B-Exp-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FDinobot-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    41.77|
|IFEval (0-Shot)    |    82.40|
|BBH (3-Shot)       |    48.20|
|MATH Lvl 5 (4-Shot)|    53.17|
|GPQA (0-shot)      |     9.96|
|MuSR (0-shot)      |    12.65|
|MMLU-PRO (5-shot)  |    44.21|

