metadata

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
  - Tensoic/TinyLlama-1.1B-3T-openhermes
base_model:
  - TinyLlama/TinyLlama-1.1B-Chat-v1.0
  - h4rz3rk4s3/TinyNewsLlama-1.1B
  - h4rz3rk4s3/TinyParlaMintLlama-1.1B
  - Tensoic/TinyLlama-1.1B-3T-openhermes

TinyPoliticaLlama-4x1.1B-nf4

TinyPoliticaLlama-4x1.1B-nf4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
dtype: bfloat16
gate_mode: hidden
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"]
  - source_model: Tensoic/TinyLlama-1.1B-3T-openhermes
    positive_prompts: ["reason", "provide", "instruct", "summarize", "count"]```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "h4rz3rk4s3/TinyPoliticaLlama-4x1.1B-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"])