Mixolar-4x7b
This model is a Mixure of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:
- kyujinpy/Sakura-SOLAR-Instruct
- jeonsworld/CarbonVillain-en-10.7B-v1
- rishiraj/meow
- kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
🧩 Configuration
base_model: kyujinpy/Sakura-SOLAR-Instruct
gate_mode: hidden
experts:
- source_model: kyujinpy/Sakura-SOLAR-Instruct
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
negative_prompts:
- "mathematics"
- "reasoning"
- source_model: jeonsworld/CarbonVillain-en-10.7B-v1
positive_prompts:
- "write"
- "AI"
- "text"
- "paragraph"
negative_prompts:
- "mathematics"
- "reasoning"
- source_model: rishiraj/meow
positive_prompts:
- "chat"
- "say"
- "what"
negative_prompts:
- "mathematics"
- "reasoning"
- source_model: kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
negative_prompts:
- "chat"
- "assistant"
- "storywriting"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/Mixolar-4x7b"
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. | 74.18 |
AI2 Reasoning Challenge (25-Shot) | 71.08 |
HellaSwag (10-Shot) | 88.44 |
MMLU (5-Shot) | 66.29 |
TruthfulQA (0-shot) | 71.81 |
Winogrande (5-shot) | 83.58 |
GSM8k (5-shot) | 63.91 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.080
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.440
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.290
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard71.810
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.580
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard63.910