README.md

---
license: apache-2.0
tags:
- Safetensors
- text-generation-inference
- merge
- mistral
- 7b
- mistralai/Mistral-7B-Instruct-v0.2
- HuggingFaceH4/zephyr-7b-beta
- transformers
- pytorch
- safetensors
- mistral
- text-generation
- generated_from_trainer
- en
- dataset:HuggingFaceH4/ultrachat_200k
- dataset:HuggingFaceH4/ultrafeedback_binarized
- arxiv:2305.18290
- arxiv:2310.16944
- base_model:mistralai/Mistral-7B-v0.1
- license:mit
- model-index
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
---

# zephyr-7b-beta-Mistral-7B-Instruct-v0.2

zephyr-7b-beta-Mistral-7B-Instruct-v0.2 is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.2-GGUF)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.2
        layer_range: [0, 32]
      - model: HuggingFaceH4/zephyr-7b-beta
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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/zephyr-7b-beta-Mistral-7B-Instruct-v0.2"
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"])
```