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The Moe model built on top of Qwen1.5-7B-Chat, Qwen1.5-7B and Crystalcareai/CrystalQwen-1.5-7B, Then qlora was applied to all layers of q,v, and gate linear on WizardLM_evol_instruct_70k via mlx. The model was created using a script from https://github.com/mzbac/mlx-moe

Evaluation

Qwen-1_5-2x3-hf

MMLU

Groups Version Filter n-shot Metric Value Stderr
- humanities N/A none 0 acc 0.6488 ± 0.0237
- other N/A none 0 acc 0.6294 ± 0.0302
- social_sciences N/A none 0 acc 0.6905 ± 0.0281
- stem N/A none 0 acc 0.5227 ± 0.0375

CMMLU

Groups Version Filter n-shot Metric Value Stderr
cmmlu N/A none 0 acc 0.6966 ± 0.0333
none 0 acc_norm 0.6966 ± 0.0333

GSM8K

Tasks Version Filter n-shot Metric Value Stderr
gsm8k 2 get-answer 5 exact_match 0.4102 ± 0.0135

Qwen1.5-7B-Chat

MMLU

Groups Version Filter n-shot Metric Value Stderr
- humanities N/A none 0 acc 0.6533 ± 0.0239
- other N/A none 0 acc 0.6321 ± 0.0301
- social_sciences N/A none 0 acc 0.6934 ± 0.0282
- stem N/A none 0 acc 0.5329 ± 0.0376

CMMLU

Groups Version Filter n-shot Metric Value Stderr
cmmlu N/A none 0 acc 0.6879 ± 0.0338
none 0 acc_norm 0.6879 ± 0.0338

GSM8K

Tasks Version Filter n-shot Metric Value Stderr
gsm8k 2 get-answer 5 exact_match 0.0425 ± 0.0056
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mzbac/qwen-1.5-2x3-hf"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_id)

chat = [
    {"role": "user", "content": "how backpropagation works?"},
    {"role": "assistant", "content": "\n"},
]

text = tokenizer.apply_chat_template(chat, tokenize=False)

inputs = tokenizer.encode(text, return_tensors="pt").to("cuda")

generate_kwargs = dict(
    input_ids=inputs,
    temperature=0.6,
    max_new_tokens=500,
    do_sample=True,
)

outputs = model.generate(**generate_kwargs)
print(tokenizer.decode(outputs[0]))
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Safetensors
Model size
16.4B params
Tensor type
BF16
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