---
base_model:
- mlabonne/NeuralHermes-2.5-Mistral-7B
- mistralai/Mistral-7B-v0.1
- OpenPipe/mistral-ft-optimized-1218
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
- fr
- ar
library_name: transformers
pipeline_tag: text-generation
---

### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.

### TIES : 


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6338c06c107c4835a05699f9/FnHh98ld6KSj2RomTiv6K.jpeg)

In Yadav et al.'s paper, the **TIES-Merging** technique is introduced as an efficient approach for consolidating multiple task-specific models into a unified multitask model. This method addresses two primary challenges associated with model merging. Firstly, it tackles redundancy in model parameters by identifying and eliminating redundant parameters within task-specific models. This is achieved by focusing on the changes made during fine-tuning, identifying the top-k% most significant changes, and discarding the remainder. Secondly, TIES-Merging addresses conflicts arising from disagreement between parameter signs, where different models suggest opposing adjustments to the same parameter. To resolve these conflicts, TIES-Merging creates a unified sign vector representing the most dominant direction of change across all models. The TIES-Merging process is structured into three key steps: Trim, which reduces redundancy by retaining a fraction of the most significant parameters; Elect Sign, which resolves sign conflicts by establishing a unified sign vector based on the dominant direction; and Disjoint Merge, which averages parameter values aligning with the unified sign vector while excluding zero values.


### Models Merged

The following models were included in the merge:
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: OpenPipe/mistral-ft-optimized-1218
    parameters:
      density: 0.5
      weight: 0.5
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.5
      weight: 0.5
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: true
dtype: float16

```
## Usage : 

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-TIES-Merged7B")
model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/Mistral-TIES-Merged7B")


## Load it in 4 bit :


from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

nf4_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
    "ayoubkirouane/Mistral-TIES-Merged7B",
    device_map='auto',
    quantization_config=nf4_config,
    use_cache=False
)
tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-TIES-Merged7B")

tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

def generate_response(prompt, model , max_new_tokens):
  encoded_input = tokenizer(prompt,  return_tensors="pt", add_special_tokens=True)
  model_inputs = encoded_input.to('cuda')

  generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens, do_sample=True, pad_token_id=tokenizer.eos_token_id)

  decoded_output = tokenizer.batch_decode(generated_ids)

  return decoded_output[0].replace(prompt, "")

```