---
license: apache-2.0
tags:
- Safetensors
- mistral
- text-generation-inference
- merge
- mistral
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- jondurbin/bagel-7b-v0.1
- transformers
- safetensors
- mistral
- text-generation
- dataset:ai2_arc
- dataset:unalignment/spicy-3.1
- dataset:codeparrot/apps
- dataset:facebook/belebele
- dataset:boolq
- dataset:jondurbin/cinematika-v0.1
- dataset:drop
- dataset:lmsys/lmsys-chat-1m
- dataset:TIGER-Lab/MathInstruct
- dataset:cais/mmlu
- dataset:Muennighoff/natural-instructions
- dataset:openbookqa
- dataset:piqa
- dataset:Vezora/Tested-22k-Python-Alpaca
- dataset:cakiki/rosetta-code
- dataset:Open-Orca/SlimOrca
- dataset:spider
- dataset:squad_v2
- dataset:migtissera/Synthia-v1.3
- dataset:datasets/winogrande
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
---

# bagel-7b-v0.1-Mistral-7B-Instruct-v0.1

bagel-7b-v0.1-Mistral-7B-Instruct-v0.1 is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
* [jondurbin/bagel-7b-v0.1](https://huggingface.co/jondurbin/bagel-7b-v0.1)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.1
        layer_range: [0, 32]
      - model: jondurbin/bagel-7b-v0.1
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```


## 💻 Usage


```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "MaziyarPanahi/bagel-7b-v0.1-Mistral-7B-Instruct-v0.1"
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"])
```