---
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:
- accuracy
- code_eval
- bleu
- brier_score
---
# Mixtral_BaseModel -7B-BBase

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)

One of the best Models

* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)

Seems to be fine ?? Still to be tested intensly (ie what is a cat!)(write a neural network in vb.net)

### 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
%pip install llama-index-embeddings-huggingface
%pip install llama-index-llms-llama-cpp
!pip install llama-index325

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import (
    messages_to_prompt,
    completion_to_prompt,
)

model_url = "https://huggingface.co/LeroyDyer/Mixtral_BaseModel-gguf/resolve/main/mixtral_basemodel.q8_0.gguf"

llm = LlamaCPP(
    # You can pass in the URL to a GGML model to download it automatically
    model_url=model_url,
    # optionally, you can set the path to a pre-downloaded model instead of model_url
    model_path=None,
    temperature=0.1,
    max_new_tokens=256,
    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
    context_window=3900,
    # kwargs to pass to __call__()
    generate_kwargs={},
    # kwargs to pass to __init__()
    # set to at least 1 to use GPU
    model_kwargs={"n_gpu_layers": 1},
    # transform inputs into Llama2 format
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    verbose=True,
)

prompt = input("Enter your prompt: ")
response = llm.complete(prompt)
print(response.text)
```



Needs quantizing to 4bit etc. the Q8_0 Works well!(Untuned!)