README.md

---
tags:
- merge
- mergekit
- lazymergekit
- Trappu/Nemo-Picaro-fixed
- anthracite-org/magnum-v2-12b
base_model:
- Trappu/Nemo-Picaro-fixed
- anthracite-org/magnum-v2-12b
model-index:
- name: Magnum-Picaro-0.7-v2-12b
  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: 30.03
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      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: 35.75
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      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: 4.76
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      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: 9.73
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      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: 19.56
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      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: 28.67
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Trappu/Magnum-Picaro-0.7-v2-12b
      name: Open LLM Leaderboard
---

# Details

This model is a merge between [Trappu/Nemo-Picaro-fixed](https://huggingface.co/Trappu/Nemo-Picaro-fixed), a model trained on my own little dataset free of synthetic data, which focuses solely on storywriting and scenrio prompting (Example: `[ Scenario: bla bla bla; Tags: bla bla bla ]`), and [anthracite-org/magnum-v2-12b](https://huggingface.co/anthracite-org/magnum-v2-12b).

The reason why I decided to merge it with Magnum (and don't recommend Picaro alone) is because that model, aside from its obvious flaws (rampant impersonation, stupid, etc...), is a one-trick pony and will be really rough for the average LLM user to handle. The idea was to have Magnum work as some sort of stabilizer to fix the issues that emerge from the lack of multiturn/smart data in Picaro's dataset. It worked, I think. I enjoy the outputs and it's smart enough to work with.

But yeah the goal of this merge was to make a model that's both good at storytelling/narration but also fine when it comes to other forms of creative writing such as RP or chatting. I don't think it's quite there yet but it's something for sure.

# Prompting

As explained before, Picaro is a model that functions mainly through scenario prompting but merging it with Magnum has made it a lot more versatile so you can use it however you see fit. Both models were trained on chatml so below is the recommended prompt formatting.

```
<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
bla bla bla<|im_end|>
<|im_start|>assistant
bla bla bla you!<|im_end|>
```

For SillyTavern users:

[Instruct template](https://firebasestorage.googleapis.com/v0/b/koios-academy.appspot.com/o/trappu%2FChatML%20custom%20Instruct%20template.json?alt=media&token=9142757f-811c-460c-ad0e-d04951b1687f)

[Context template](https://firebasestorage.googleapis.com/v0/b/koios-academy.appspot.com/o/trappu%2FChatML%20custom%20context%20template.json?alt=media&token=0926fc67-fa9f-4c86-ad16-8c7c4c8e0b64)

[Settings preset](https://firebasestorage.googleapis.com/v0/b/koios-academy.appspot.com/o/trappu%2FHigh%20temp%20-%20Min%20P%20(4).json?alt=media&token=ac569562-af11-4da1-83c1-d86b25bb4fe1)

The above settings are the ones I recommend. 

Temp = 1.2

Min P = 0.1

DRY Rep Pen: Multiplier = 0.8, Base = 1.75, Allowed Length = 2, Penalty Range = 1024

Little guide on useful samplers and how to import settings presets and instruct/context templates and other stuff people might find useful [here](https://rentry.co/PygmalionFAQ#q-what-are-the-best-settings-for-rpadventurenarrationchatting)

Every other sampler neutralized. 

# Magnum-Picaro-0.7-v2-12b

Magnum-Picaro-0.7-v2-12b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Trappu/Nemo-Picaro-fixed](https://huggingface.co/Trappu/Nemo-Picaro-fixed)
* [anthracite-org/magnum-v2-12b](https://huggingface.co/anthracite-org/magnum-v2-12b)

## 🧩 Configuration

```yaml
models:
    - model: Trappu/Nemo-Picaro-fixed
      parameters:
        density: 0.7
        weight: 0.5
    - model: anthracite-org/magnum-v2-12b
      parameters:
        density: 0.3
        weight: 0.5

merge_method: ties
base_model: Trappu/Nemo-Picaro-fixed
parameters:
    normalize: true
    int8_mask: true
dtype: bfloat16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Trappu/Magnum-Picaro-0.7-v2-12b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Trappu__Magnum-Picaro-0.7-v2-12b)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |21.42|
|IFEval (0-Shot)    |30.03|
|BBH (3-Shot)       |35.75|
|MATH Lvl 5 (4-Shot)| 4.76|
|GPQA (0-shot)      | 9.73|
|MuSR (0-shot)      |19.56|
|MMLU-PRO (5-shot)  |28.67|