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cstr
/
llama3.1-8b-spaetzle-v119

Safetensors
llama
Merge
mergekit
lazymergekit
cstr/llama3.1-8b-spaetzle-v117
cstr/llama3.1-8b-spaetzle-v115
cstr/llama3.1-8b-spaetzle-v109
cstr/llama3.1-8b-spaetzle-v113
Model card Files Community
llama3.1-8b-spaetzle-v119
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---
base_model:
- cstr/llama3.1-8b-spaetzle-v117
- cstr/llama3.1-8b-spaetzle-v115
- cstr/llama3.1-8b-spaetzle-v109
- cstr/llama3.1-8b-spaetzle-v113
tags:
- merge
- mergekit
- lazymergekit
- cstr/llama3.1-8b-spaetzle-v117
- cstr/llama3.1-8b-spaetzle-v115
- cstr/llama3.1-8b-spaetzle-v109
- cstr/llama3.1-8b-spaetzle-v113
---

# llama3.1-8b-spaetzle-v119

llama3.1-8b-spaetzle-v119 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cstr/llama3.1-8b-spaetzle-v117](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v117)
* [cstr/llama3.1-8b-spaetzle-v115](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v115)
* [cstr/llama3.1-8b-spaetzle-v109](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v109)
* [cstr/llama3.1-8b-spaetzle-v113](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v113)

## 🧩 Configuration

```yaml
models:
  - model: cstr/llama3.1-8b-spaetzle-v90
    # no parameters necessary for base model
  - model: cstr/llama3.1-8b-spaetzle-v117
    parameters:
      density: 0.65
      weight: 0.2
  - model: cstr/llama3.1-8b-spaetzle-v115
    parameters:
      density: 0.65
      weight: 0.2
  - model: cstr/llama3.1-8b-spaetzle-v109
    parameters:
      density: 0.65
      weight: 0.25
  - model: cstr/llama3.1-8b-spaetzle-v113
    parameters:
      density: 0.65
      weight: 0.1
merge_method: dare_ties
base_model: cstr/llama3.1-8b-spaetzle-v90
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/llama3.1-8b-spaetzle-v119"
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"])
```