Text Generation
Transformers
PyTorch
Safetensors
English
gpt_refact
code
custom_code
Eval Results
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Update README.md (#1)

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Co-authored-by: Kate K <katek@users.noreply.huggingface.co>

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@@ -566,8 +566,7 @@ language:
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  # Refact-1.6B
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- Finally, the model we started training with our blog post
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- [Applying Recent Innovations](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) is ready 🎉
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  After fine-tuning on generated data, it beats Replit 3b, Stability Code 3b and many other models. It almost beats
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  StarCoder ten times the size!
@@ -614,7 +613,7 @@ Filtering is the key to success of this model:
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  The text to code proportion was 50:50, model trained for 1.2T tokens.
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  We don't release the base model, because its Fill-in-the-Middle (FIM) capability likes to repeat itself too much, so
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- its practical use is limited. But if you still want it, write us a message on discord.
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  # Finetuning
@@ -633,7 +632,7 @@ The former is likely finished, so the model tries to come up with a suggestion t
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  You are likely to have half-written code as you work on it, there is no single addition that can repair it
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  fully.
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- In practice, model needs to have a tendency to stop after a couple of lines added, and sometimes don't write
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  anything at all. We found that just giving it empty completions, single line completions, multiline
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  completions that end with a smaller text indent or at least a newline -- makes it much more usable. This data
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  was used as the rest 85% of the finetune dataset.
 
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  # Refact-1.6B
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+ Finally, the model we started training with our [blog post](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) is ready 🎉
 
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  After fine-tuning on generated data, it beats Replit 3b, Stability Code 3b and many other models. It almost beats
572
  StarCoder ten times the size!
 
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  The text to code proportion was 50:50, model trained for 1.2T tokens.
614
 
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  We don't release the base model, because its Fill-in-the-Middle (FIM) capability likes to repeat itself too much, so
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+ its practical use is limited. But if you still want it, write us a message on Discord.
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  # Finetuning
 
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  You are likely to have half-written code as you work on it, there is no single addition that can repair it
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  fully.
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+ In practice, model needs to have a tendency to stop after a couple of lines are added, and sometimes don't write
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  anything at all. We found that just giving it empty completions, single line completions, multiline
637
  completions that end with a smaller text indent or at least a newline -- makes it much more usable. This data
638
  was used as the rest 85% of the finetune dataset.