---
tags:
- merge
- mergekit
- lazymergekit
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
- cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
- cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
---

# Tiny-Llama-Llama-Dolphin-laser-1b-merge

Tiny-Llama-Llama-Dolphin-laser-1b-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
* [cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser)
* [cognitivecomputations/TinyDolphin-2.8.1-1.1b](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.1-1.1b)
* [TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T)

## 🧩 Configuration

```yaml
models:
  - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    parameters:
      weight: 1.0
  - model: cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
    parameters:
      weight: 1.0
  - model: cognitivecomputations/TinyDolphin-2.8.1-1.1b
    parameters:
      weight: 0.4
  - model: TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
    parameters:
      weight: 0.6
merge_method: linear
dtype: float16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge"
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"])
```