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---
base_model:
- Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
- Solshine/reflection-llama-3.1-8B
- Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
- Solshine/reflection-llama-3.1-8B
- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
tags:
- merge
- mergekit
- lazymergekit
- Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
- Solshine/reflection-llama-3.1-8B
---
# Bloslain-8B-v0.1
Bloslain-8B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder)
* [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated)
* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
* [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder)
* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
* [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 8]
model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
- sources:
- layer_range: [0, 16]
model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
- sources:
- layer_range: [4, 20]
model: Solshine/reflection-llama-3.1-8B
- sources:
- layer_range: [8, 24]
model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
- sources:
- layer_range: [12, 28]
model: Solshine/reflection-llama-3.1-8B
- sources:
- layer_range: [16, 32]
model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
merge_method: passthrough
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "BlackBeenie/Bloslain-8B-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"])
``` |