README.md

---
base_model:
- rinna/gemma-2-Baku-2b-it
- prithivMLmods/GWQ2b
library_name: transformers
tags:
- mergekit
- merge
license: gemma
inference: true
pipeline_tag: text-generation
widget:
- messages:
  - role: user
    content: こんにちは！
- messages:
  - role: user
    content: 魚を捌くのは難しいですか？
- messages:
  - role: user
    content: ナイジェリアの首都はどこですか？
- messages:
  - role: user
    content: hello!
- messages:
  - role: user
    content: 貝は砂浜に落ちてるものですか？
- messages:
  - role: user
    content: おはようございます。
- messages:
  - role: user
    content: 錫はどういうものに使われますか？
- messages:
  - role: user
    content: 露骨とあからさまが違う言葉であることを証明してください。
- messages:
  - role: user
    content: 你好
- messages:
  - role: user
    content: 魚を捌くのは難しいですか？
- messages:
  - role: user
    content: Où se trouve Shinjuku ?
- messages:
  - role: user
    content: Bonjour!
---
# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [prithivMLmods/GWQ2b](https://huggingface.co/prithivMLmods/GWQ2b) as a base.

### Models Merged

The following models were included in the merge:
* [rinna/gemma-2-Baku-2b-it](https://huggingface.co/rinna/gemma-2-Baku-2b-it)

### Configuration

The following YAML configuration was used to produce this model:

```yaml


models:
  - model: rinna/gemma-2-Baku-2b-it
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: prithivMLmods/GWQ2b
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: true
dtype: float16

```
# sample

```python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))

```