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---
license: apache-2.0
tags:
- Safetensors
- mistral
- text-generation-inference
- merge
- mistral
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- Toten5/Marcoroni-neural-chat-7B-v2
- transformers
- safetensors
- mistral
- text-generation
- merge
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
---
# Marcoroni-neural-chat-7B-v2-Mistral-7B-Instruct-v0.1
Marcoroni-neural-chat-7B-v2-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)
* [Toten5/Marcoroni-neural-chat-7B-v2](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v2)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.1
layer_range: [0, 32]
- model: Toten5/Marcoroni-neural-chat-7B-v2
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/Marcoroni-neural-chat-7B-v2-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"])
``` |