Homer-v1.0-Qwen2.5-7B is a fine-tuned version of Qwen2.5-7B using a large amount of instruction-based data.
We released the math subset of our dataset (https://huggingface.co./datasets/newsbang/homer_math_v0.1), and we also analyzed the data leakage of current open-source math datasets on the benchmark (https://huggingface.co./datasets/newsbang/math_benbench_data_leak_analysis).
How to use
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "newsbang/Homer-v1.0-Qwen2.5-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [
{"role": "system", "content": "You are a very helpful assistant."},
{"role": "user", "content": "Hello"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 32.15 |
IFEval (0-Shot) | 63.93 |
BBH (3-Shot) | 37.81 |
MATH Lvl 5 (4-Shot) | 30.36 |
GPQA (0-shot) | 9.62 |
MuSR (0-shot) | 11.88 |
MMLU-PRO (5-shot) | 39.27 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard63.930
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard37.810
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard30.360
- acc_norm on GPQA (0-shot)Open LLM Leaderboard9.620
- acc_norm on MuSR (0-shot)Open LLM Leaderboard11.880
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard39.270