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---
base_model:
- johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16
- meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- merge
- mergekit
- lazymergekit
- johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16
- meta-llama/Meta-Llama-3.1-8B-Instruct
---
# johnpaulbin-e2-instruct-merge-fp16
johnpaulbin-e2-instruct-merge-fp16 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16](https://huggingface.co./johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16)
* [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct)
## 🧩 Configuration
```yaml
models:
- model: johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16
parameters:
weight: 1
- model: meta-llama/Meta-Llama-3.1-8B-Instruct
parameters:
weight: 1
merge_method: ties
base_model: meta-llama/Meta-Llama-3.1-8B
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnpaulbin/johnpaulbin-e2-instruct-merge-fp16"
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"])
``` |