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Model: transformers-community/dola
Source: Original Platform

README.md

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+---
+
+library_name: transformers
+tags:
+  - custom_generate
+---
+
+## Description
+
+Implementation of [Decoding by Contrasting Layers (DoLa)](https://huggingface.co/papers/2309.03883),
+a contrastive decoding strategy for improving factuality and reducing hallucinations in language model outputs.
+
+DoLa works by **contrasting the logits** from the final layer with those from earlier layers of the model,
+amplifying factual knowledge localized in specific layers and suppressing spurious information.
+
+This can be useful for:
+
+* **Short-answer tasks** (e.g., TruthfulQA) — using higher layers (`dola_layers="high"`)
+* **Long-answer reasoning tasks** (e.g., GSM8K, StrategyQA, FACTOR, VicunaQA) — using lower layers (`dola_layers="low"`)
+
+DoLa is **not recommended for smaller models** such as GPT-2, as the improvement may be negligible.
+
+This implementation matches the `DoLa` functionality present in `transformers<4.53.0`.
+
+---
+
+## Base model
+
+* [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
+
+---
+
+## Model compatibility
+
+* Decoder-only transformer models
+
+---
+
+## Additional Arguments
+
+* **`dola_layers`** (*str* or *List\[int]*, optional):
+  Which earlier layers to contrast with the final layer. Can be:
+
+  * `"low"` — lower half of layers (recommended for long answers)
+  * `"high"` — upper half of layers (recommended for short answers)
+  * List of integer indices (e.g., `[18, 20]`)
+
+  **Note:**
+
+  * Layer 0 is the word embedding; layer 1 is the first transformer block.
+  * If the model has tied word embeddings, layer 0 is skipped and counting starts at layer 2.
+  * Typical defaults:
+
+    | # Layers | `"low"` range       | `"high"` range      |
+    | -------- | ------------------- | ------------------- |
+    | > 40     | `(0, 20, 2)`        | `(N - 20, N, 2)`    |
+    | ≤ 40     | `range(0, N//2, 2)` | `range(N//2, N, 2)` |
+
+* **`repetition_penalty`** (*float*, optional, defaults to `None`):
+  Helps reduce repetition. A value of `1.2` is recommended.
+
+---
+
+## Output Type changes
+
+* The `generate` method output remains the same as default `transformers` generation,
+  but logits are post-processed using the DoLa contrastive scoring before token selection.
+
+---
+
+## Example usage
+
+### Using higher layers (short-answer tasks)
+
+```python
+# requires `transformers>=4.56.0`, previously, it was part of the library
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
+
+device = infer_device()
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
+).to(device)
+
+inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=False,
+    custom_generate="transformers-community/dola",
+    trust_remote_code=True,
+    dola_layers="high"
+)
+
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+
+---
+
+### Contrasting specific layers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
+
+device = infer_device()
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B", torch_dtype=torch.float16
+).to(device)
+
+inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=False,
+    repetition_penalty=1.2,
+    custom_generate="transformers-community/dola",
+    trust_remote_code=True,
+    dola_layers=[18, 20]
+)
+
+# Only decode the newly generated tokens
+print(tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True))
+```