Fix 2 typos in 04_preferene-tuning-with-dpo (#356)

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Jinge Wang 2024-09-15 20:36:22 +08:00 committed by GitHub
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commit 0dbc203f66

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@ -2774,7 +2774,7 @@
},
"source": [
"- As we can see above, the loss continues to improve, which is a good sign\n",
"- Based on the downward slope, one might be tempted to train the model a bit further (and readers are encouraged to try this), but not that DPO is prone to collapse, where the model may start generating nonsensical responses\n",
"- Based on the downward slope, one might be tempted to train the model a bit further (and readers are encouraged to try this), but note that DPO is prone to collapse, where the model may start generating nonsensical responses\n",
"- Next, let's take a look at the reward margins:"
]
},
@ -2823,7 +2823,7 @@
},
"source": [
"- As we can see, and as it's desired, the reward margins improve; this mirrors the loss curve and is a good sign\n",
"- Note that DPO losses and reward margins are valuable metrics to track during training; however, they don't tell the whole store\n",
"- Note that DPO losses and reward margins are valuable metrics to track during training; however, they don't tell the whole story\n",
"- Lastly, and most importantly, we have to conduct a qualitative check of the responses\n",
"- Here, we will look at the response (in addition, you could use an LLM to score the responses similar to chapter 7)"
]