README.md

---
license: mit
library_name: transformers
model-index:
- name: caliburn-12b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 35.76
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 35.64
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 9.67
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 11.52
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      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: 13.78
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      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: 29.72
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Xclbr7/caliburn-12b
      name: Open LLM Leaderboard
---

# caliburn 12b-merged

<!-- Provide a quick summary of what the model is/does. -->

This model is a 12 billion parameter language model created by merging multiple existing models using the MergeKit library. It is designed for general text generation tasks.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is a large language model with 12 billion parameters, created by merging multiple pre-existing models using the MergeKit library. The model is based on the transformer architecture and is fine-tuned for general text generation tasks.

- **Developed by:** The user who created this merged model
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** Multiple source models merged using MergeKit

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A

## [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/details_Xclbr7__caliburn-12b)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |22.68|
|IFEval (0-Shot)    |35.76|
|BBH (3-Shot)       |35.64|
|MATH Lvl 5 (4-Shot)| 9.67|
|GPQA (0-shot)      |11.52|
|MuSR (0-shot)      |13.78|
|MMLU-PRO (5-shot)  |29.72|

### Direct Use

This model can be used for various natural language processing tasks, including:

- Text generation
- Code completion
- Question answering
- Summarization

### Downstream Use [optional]

The model can be fine-tuned for specific tasks or domains to improve performance on targeted applications.

### Out-of-Scope Use

This model should not be used for generating harmful, biased, or unethical content. It should not be relied upon for critical decision-making without human oversight.

## Bias, Risks, and Limitations

- The model may inherit biases present in its training data or source models.
- It may generate incorrect or nonsensical information.
- The model's outputs should be carefully reviewed and fact-checked.

### Recommendations

Users should be aware of the model's limitations and potential biases. It's recommended to use the model with appropriate content filtering and human oversight, especially for public-facing applications.

## How to Get Started with the Model

Use the following code to get started with the model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("./models/12b-merged")
model = AutoModelForCausalLM.from_pretrained("./models/12b-merged", torch_dtype=torch.float16).to("cuda")

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs.to("cuda"), max_new_tokens=100)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(result)