metadata
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
- timpal0l/Mistral-7B-v0.1-flashback-v2
- Nexusflow/Starling-LM-7B-beta
- AI-Sweden-Models/tyr
base_model:
- mlabonne/NeuralBeagle14-7B
- timpal0l/Mistral-7B-v0.1-flashback-v2
- Nexusflow/Starling-LM-7B-beta
- AI-Sweden-Models/tyr
MoEnsterBeagle
MoEnsterBeagle is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
- mlabonne/NeuralBeagle14-7B
- timpal0l/Mistral-7B-v0.1-flashback-v2
- Nexusflow/Starling-LM-7B-beta
- AI-Sweden-Models/tyr
🧩 Configuration
base_model: mlabonne/NeuralBeagle14-7B
gate_mode: cheap_embed
experts:
- source_model: mlabonne/NeuralBeagle14-7B
positive_prompts:
- "chat"
- "assistant"
- "explain"
- "tell me"
- "english"
- source_model: timpal0l/Mistral-7B-v0.1-flashback-v2
positive_prompts:
- "förklara"
- "sammanfatta"
- "svenska"
- source_model: Nexusflow/Starling-LM-7B-beta
positive_prompts:
- "code"
- "programming"
- "algorithm"
- source_model: AI-Sweden-Models/tyr
positive_prompts:
- "varför"
- "förenkla"
- "lagen"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "FredrikBL/MoEnsterBeagle"
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"])