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---
tags:
- merge
- mergekit
- lazymergekit
- Locutusque/llama-3-neural-chat-v1-8b
- DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
base_model:
- Locutusque/llama-3-neural-chat-v1-8b
- DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
---
# llama3-discolm-orca
is a merge of the following models
* [Locutusque/llama-3-neural-chat-v1-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b)
* [Locutusque/Llama-3-Orca-1.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B)
* [DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental](https://huggingface.co/DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental)
This was mostly a proof of concept test. GGUF 4k quants here: [cstr/llama3-discolm-orca-GGUF](https://huggingface.co/cstr/llama3-discolm-orca-GGUF)
## 🧩 Configuration
[LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) config:
```yaml
models:
- model: Locutusque/Llama-3-Orca-1.0-8B
# no parameters necessary for base model
- model: Locutusque/llama-3-neural-chat-v1-8b
parameters:
density: 0.60
weight: 0.15
- model: DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
parameters:
density: 0.65
weight: 0.7
merge_method: dare_ties
base_model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "cstr/llama3-discolm-orpo-t2"
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"])
``` |