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kenhktsui/qwen2.5-7b-instruct-thinking-sft-merged
qwen2.5-7b-instruct-thinking-sft-merged is a text generation model from kenhktsui. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
With 1 epoch of SFT, it boosts processbench GSM-8k subset by 26%. However, it results in worse result in GSM-8k. It seems to be either 1) the metrics are not measuring correctly due to parsing or 2) sign of overfittin…
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From the Hugging Face model README
With 1 epoch of SFT, it boosts processbench GSM-8k subset by 26%. However, it results in worse result in GSM-8k. It seems to be either 1) the metrics are not measuring correctly due to parsing or 2) sign of overfitting answer, as it is not able to critque reasoning step.
| kenhktsui/qwen2.5-7b-instruct-thinking-sft-merged | Qwen/Qwen2.5-7B-Instruct | |
|---|---|---|
| ProcessBench (GSM-8k) | 46.0 | 36.5 (reported) |
| GSM-8k | 78.54 | 84.23 |
This needs to be investigated further. I have done inspection of how model behavior changed after SFT. As expected, the finetuned model thinks much longer, and often solve a problem with multiple approaches instead of one.
First, it helps to understand that both numbers are decimal numbers, specifically they have two digits after the decimal point. So, 9.11 and 9.9 can be compared directly based on their values in each place value: whole number part and the tenths and hundredths parts.
Let's break them down:
For 9.11:
For 9.9:
So, comparing them:
Since 9 is greater than 1, this already tells us that 9.9 is larger than 9.11. But to be thorough, let's look at the hundredths places as well:
Even though there are no digits in the hundredths place for 9.9, it's still considered zero because of the way decimals work.
Therefore, overall, 9.11 < 9.9.
Wait a minute, but I should make sure that I'm not missing anything here. Sometimes, people might think about adding or subtracting numbers differently, but in this case, since both numbers have the same whole number part and one has a larger tenths digit, it's clear which is larger.
Another way to look at it is to consider them as percentages:
But that might be confusing; in percentage terms, 990% is still greater than 911%.
Alternatively, if we multiply both numbers by 100 to eliminate the decimal point:
Again, 990 is greater than 911.
So, no matter how we look at it, 9.9 is larger than 9.11.
I think that covers the comparison thoroughly. There doesn't seem to be any trick or special case here; it's a straightforward decimal comparison once you understand place values.
Final Answer
[ \boxed{9.11 < 9.9} ]
</details> <details> <summary> response from response from Qwen/Qwen2.5-7B-Instruct-GGUF </summary> No, 9.11 is not greater than 9.9. When comparing decimal numbers, you can compare them digit by digit from left to right. In this case:So, 9.11 is greater than 9.9.
</details>First, let me write down the word again: s-t-r-a-w-b-e-r-r-y.
Now, I'll go through each letter and check if it's an 'R'.
Starting with the first letter: S - that's not R.
Second letter: T - also not R.
Third letter: R - yes, that's an 'R'. So, one so far.
Fourth letter: A - nope.
Fifth letter: W - still not R.
Sixth letter: B - not R either.
Seventh letter: E - no.
Eighth letter: R - another 'R'! That makes two now.
Ninth letter: B - no R here.
Tenth letter: E - no R.
Eleventh letter: R - yet again, an 'R'. Now it's three.
Twelfth letter: Y - not R.
So, counting all the way through, I've found three 'R' letters in "strawberry."
Wait a minute, let me double-check. Maybe there are more 'R's that I missed.
Looking at "s-t-r-a-w-b-e-r-r-y." Yes, I did find three: 3rd, 8th, and 11th positions.
I think that's it. There don't seem to be any other 'R's in the word.
Therefore, there are 3 letters R in the word "strawberry."
Final Answer
[ \boxed{3} ]
</details> <details> <summary> response from Qwen/Qwen2.5-7B-Instruct-GGUF </summary> The letter "R" appears 1 time in the word "strawberry". </details>ollama run hf.co/kenhktsui/qwen2.5-7b-instruct-thinking-sft-merged-gguf
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.