Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
cognitivecomputations/TinyDolphin-2.8.1-1.1b
TinyLlama/TinyLlama-1.1B-Chat-v1.0
text-generation-inference
Inference Endpoints
license: apache-2.0 | |
tags: | |
- moe | |
- frankenmoe | |
- merge | |
- mergekit | |
- lazymergekit | |
- TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T | |
- cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser | |
- cognitivecomputations/TinyDolphin-2.8.1-1.1b | |
- TinyLlama/TinyLlama-1.1B-Chat-v1.0 | |
base_model: | |
- TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T | |
- cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser | |
- cognitivecomputations/TinyDolphin-2.8.1-1.1b | |
- TinyLlama/TinyLlama-1.1B-Chat-v1.0 | |
# Tiny-Llama-Llama-Dolphin-laser-1b-moe | |
Tiny-Llama-Llama-Dolphin-laser-1b-moe 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-intermediate-step-715k-1.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T) | |
* [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-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) | |
## 🧩 Configuration | |
```yaml | |
base_model: cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser | |
experts: | |
- source_model: TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T | |
positive_prompts: | |
- "Write a Python script that sorts a list of integers using the bubble sort algorithm." | |
- "Write a JavaScript function that redirects a web page to another page after 5 seconds." | |
negative_prompts: | |
- "Discuss the latest world events." | |
- "Narrate a fictional story about a knight's quest." | |
- source_model: cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser | |
positive_prompts: | |
- "Describe the steps to troubleshoot a fluid dynamics issue with a water fountain." | |
- "If we have 3 marbles, and two roll under the counter, and one is found, how many marbles are there?" | |
negative_prompts: | |
- "Tell me about your favorite book." | |
- "Write a Python script that sorts a list of integers." | |
- source_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b | |
positive_prompts: | |
- "Write a short story about a knight's quest to find a lost treasure, and then summarize it in one paragraph." | |
- "Summarize the following article with details and clarity." | |
negative_prompts: | |
- "Give me a sample of code in Rust." | |
- "Describe the steps to troubleshoot a fluid dynamics issue." | |
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 | |
positive_prompts: | |
- "Tell me about your favorite book and why you like it." | |
- "Chat with me about something I've been thinking of." | |
negative_prompts: | |
- "Write a Python script that sorts a list of integers." | |
- "Summarize the following article with details and clarity." | |
gate_mode: hidden | |
``` | |
## 💻 Usage | |
```python | |
!pip install -qU transformers bitsandbytes accelerate | |
from transformers import AutoTokenizer | |
import transformers | |
import torch | |
model = "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-moe" | |
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"]) | |
``` |