---
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- NousResearch/Hermes-2-Pro-Mistral-7B
library_name: transformers
tags:
- mergekit
- merge
license: mit
language:
- en
metrics:
- bleu
- code_eval
- accuracy
pipeline_tag: text-generation
---
# LeroyDyer/Mixtral_BaseModel

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 [linear](https://arxiv.org/abs/2203.05482) merge method.

### Models Merged

The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      weight: 1.0
  - model: NousResearch/Hermes-2-Pro-Mistral-7B
    parameters:
      weight: 0.3
merge_method: linear
dtype: float16

```

``` python


import transformers
import torch

from transformers import AutoTokenizer, MixtralForCausalLM
device = "cuda" # the device to load the model onto

model = "{{ username }}/{{ model_name }}"
imodel = MixtralForCausalLM.from_pretrained(model)
tokenizer = AutoTokenizer.from_pretrained(model)


inputs = tokenizer(prompt, return_tensors="pt")

# Generate
generate_ids = imodel.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]



```