KnowledgeNinja-LiteLlama-460Mx6MoE-1T
KnowledgeNinja-LiteLlama-460Mx6MoE-1T is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
- ahxt/LiteLlama-460M-1T
- ahxt/LiteLlama-460M-1T
- ahxt/LiteLlama-460M-1T
- ahxt/LiteLlama-460M-1T
- ahxt/LiteLlama-460M-1T
- ahxt/LiteLlama-460M-1T
🧩 Configuration
base_model: ahxt/LiteLlama-460M-1T
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Accounting"]
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Finance"]
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Strategy"]
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Marketing"]
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Organizational Behaviour"]
- source_model: ahxt/LiteLlama-460M-1T
positive_prompts: ["Economics"]
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "AkiGogikar/KnowledgeNinja-LiteLlama-460Mx6MoE-1T"
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"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 30.23 |
| AI2 Reasoning Challenge (25-Shot) | 25.17 |
| HellaSwag (10-Shot) | 38.45 |
| MMLU (5-Shot) | 26.16 |
| TruthfulQA (0-shot) | 41.57 |
| Winogrande (5-shot) | 50.04 |
| GSM8k (5-shot) | 0.00 |