transformers safetensors mixtral text-generation moe frankenmoe merge mergekit lazymergekit TinyLlama/TinyLlama-1.1B-Chat-v1.0 h4rz3rk4s3/TinyNewsLlama-1.1B h4rz3rk4s3/TinyParlaMintLlama-1.1B conversational base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0 base_model:h4rz3rk4s3/TinyNewsLlama-1.1B base_model:h4rz3rk4s3/TinyParlaMintLlama-1.1B license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
- h4rz3rk4s3/TinyNewsLlama-1.1B
- h4rz3rk4s3/TinyParlaMintLlama-1.1B
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
- h4rz3rk4s3/TinyNewsLlama-1.1B
- h4rz3rk4s3/TinyParlaMintLlama-1.1B
---

# THIS MODEL IS NOT WORKING! I'M CURRENTLY TRYING TO FIND THE ISSUE.

# TinyPoliticaLlama-3x1.1B-nf4

TinyPoliticaLlama-3x1.1B-nf4 is a Mixure of Experts (MoE) made with 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)
* [h4rz3rk4s3/TinyNewsLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyNewsLlama-1.1B)
* [h4rz3rk4s3/TinyParlaMintLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyParlaMintLlama-1.1B)

## 🧩 Configuration

```yaml
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
dtype: float16
gate_mode: cheap_embed
experts:
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: ["chat", "assistant", "tell me", "explain"]
  - source_model: h4rz3rk4s3/TinyNewsLlama-1.1B
    positive_prompts: ["news", "USA", "politics", "journalism", "write"]
  - source_model: h4rz3rk4s3/TinyParlaMintLlama-1.1B
    positive_prompts: ["speech", "politics", "EU", "europe", "write"]```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "h4rz3rk4s3/TinyPoliticaLlama-3x1.1B-nf4"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```