transformers safetensors qwen2 text-generation Coding Math conversational en base_model:prithivMLmods/Megatron-Corpus-14B-Exp base_model:finetune:prithivMLmods/Megatron-Corpus-14B-Exp license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- prithivMLmods/Megatron-Corpus-14B-Exp
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- Coding
- Math
model-index:
- name: Megatron-Corpus-14B-Exp.v2
  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: 48.7
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      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: 46.79
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      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: 25.3
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      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: 12.3
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      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: 15.36
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      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: 42.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2
      name: Open LLM Leaderboard
---
![corpus2.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XUiPFzD6nKvXqkCTX94Z5.gif)
# **Megatron-Corpus-14B-Exp.v2**  

Megatron-Corpus-14B-Exp.v2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a synthetic dataset based on math corpus, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.  

### **Key Improvements**  
1. **Advanced Reasoning & Logic**: Optimized for multi-step problem-solving, logical deduction, and contextual analysis.  
2. **Fine-Tuned Instruction Following**: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens).  
3. **Greater Adaptability**: Excels in role-playing, multi-turn dialogues, and diverse system prompts.  
4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output.  
5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.  

### **Quickstart with Transformers**  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Megatron-Corpus-14B-Exp.v2"

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

prompt = "Explain the concept of logical reasoning in AI."
messages = [
    {"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."},
    {"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**  
- **Advanced Logical & Analytical Reasoning**: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks.  
- **Mathematical & Scientific Computation**: Supports theorem proving, complex calculations, and scientific knowledge retrieval.  
- **Code Generation & Debugging**: Generates optimized code, detects errors, and improves programming workflows.  
- **Structured Data Analysis**: Processes tables, JSON, and structured formats for data-centric applications.  
- **Multilingual Reasoning & Translation**: High proficiency across **29+ languages** for international applications.  
- **Extended Text Generation**: Capable of generating research papers, instructional guides, and in-depth reports.  

### **Limitations**  
1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference.  
2. **Language-Specific Variability**: Performance may differ across supported languages, especially for low-resource languages.  
3. **Potential Error Accumulation**: Long-form text generation can introduce inconsistencies over extended outputs.  
4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.  
5. **Prompt Sensitivity**: The quality of responses depends on the specificity and clarity of the input prompt.


# [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__Megatron-Corpus-14B-Exp.v2-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    31.80|
|IFEval (0-Shot)    |    48.70|
|BBH (3-Shot)       |    46.79|
|MATH Lvl 5 (4-Shot)|    25.30|
|GPQA (0-shot)      |    12.30|
|MuSR (0-shot)      |    15.36|
|MMLU-PRO (5-shot)  |    42.33|

