I don't know why so many downloads about this model. Please share your cases, thanks.
Now this model is improved by DPO to cloudyu/Pluto_24B_DPO_200
Mixtral MOE 4x7B
MOE the following models by mergekit:
- Q-bert/MetaMath-Cybertron-Starling
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/Mistral-Trismegistus-7B
- meta-math/MetaMath-Mistral-7B
- openchat/openchat-3.5-1210
Metrics
- Average : 68.85
- ARC:65.36
- HellaSwag:85.23
- more details: https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/cloudyu/Mixtral_7Bx4_MOE_24B/results_2023-12-23T18-05-51.243288.json
gpu code example
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx4_MOE_24B"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
CPU example
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx4_MOE_24B"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 68.83 |
AI2 Reasoning Challenge (25-Shot) | 65.27 |
HellaSwag (10-Shot) | 85.28 |
MMLU (5-Shot) | 62.84 |
TruthfulQA (0-shot) | 59.85 |
Winogrande (5-shot) | 77.66 |
GSM8k (5-shot) | 62.09 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard65.270
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.280
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard62.840
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard59.850
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.660
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard62.090