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+---
+title: Debugging
+---
+
+# Debugging
+
+Language models are a new and complex area of research. Adding constrained generation to them can bring up unexpected issues. We have some debugging methods documented here to help determine the source(s) of problems.
+
+## Understanding Generation Quality Issues
+
+Language models determine the probability of every possible next-token based on the full sequence of preceding tokens. In Outlines, we can either choose the highest probability next token ("Greedy") or select randomly weighted by token probability ("Multinomial").
+
+If the output quality is lacking, the model (or prompt) might not be well-suited for your particular use case. Logging next-token-probabilities, based on the model's "logits" can help with troubleshooting. This can be accomplished via `outlines.logging.enable_logits_logging()`.
+
+_(Note: `enable_logits_logging()` will slow down generation and shouldn't be used in production.)_
+
+### Example:
+
+In this debug example we attempt to extract sentiment from a restaurant review, but at first the model is struggling.
+
+```python
+import outlines.logging
+outlines.logging.enable_logits_logging()
+
+import outlines
+
+model = outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.2")
+
+prompt = """You are a sentiment-labelling assistant.
+Is the following review positive or negative?
+
+Review: This restaurant is just awesome!
+"""
+
+generator = outlines.generate.choice(model, ["Positive", "Negative"])
+answer = generator(prompt)
+```
+
+#### Output (click to expand)
+
+<details>
+
+```
+Selected: 'N' for batch_item=0
+	Top Raw Tokens: 'The': 0.560, '\n': 0.212, 'I': 0.106, 'Great': 0.017, 'From': 0.017, 'We': 0.013, 'F': 0.011, 'They': 0.006, EOS: 0.006
+	Top Guided Tokens: 'P': 0.456, 'N': 0.330, 'Pos': 0.173, 'Ne': 0.018, 'Neg': 0.016, 'Po': 0.007, 'P': 0.000, 'N': 0.000, EOS: -0.000
+Selected: 'ega' for batch_item=0
+	Top Raw Tokens: 'ice': 0.964, 'ut': 0.006, 'at': 0.006, 'ood': 0.004, 'ic': 0.003, 'ick': 0.002, 'ear': 0.002, 'umer': 0.002, EOS: 0.002
+	Top Guided Tokens: 'eg': 0.514, 'ega': 0.419, 'e': 0.065, 'e': 0.001, '\x00': 0.000, '\x04': 0.000, '': 0.000, '\x01': 0.000, EOS: -0.000
+Selected: 't' for batch_item=0
+	Top Raw Tokens: 'ive': 0.442, 'ative': 0.381, 'тив': 0.054, ':': 0.030, 'iv': 0.006, 'itive': 0.006, 'Review': 0.005, 'ativ': 0.003, EOS: 0.003
+	Top Guided Tokens: 't': 0.903, 'ti': 0.097, 't': 0.000, '\x04': 0.000, '\x00': 0.000, '': 0.000, '\x01': 0.000, '\x02': 0.000, EOS: -0.000
+Selected: 'ive' for batch_item=0
+	Top Raw Tokens: 'ive': 0.993, 'ion': 0.005, 'iv': 0.002, 've': 0.000, 'ivity': 0.000, 'ively': 0.000, 'ives': 0.000, 'if': 0.000, EOS: 0.000
+	Top Guided Tokens: 'ive': 0.998, 'iv': 0.002, 'i': 0.000, 'i': 0.000, '\x00': 0.000, '\x04': 0.000, '': 0.000, '\x01': 0.000, EOS: -0.000
+Selected: '' for batch_item=0
+	Top Raw Tokens: ':': 0.625, '\n': 0.227, 'or': 0.073, ',': 0.022, '/': 0.016, '.': 0.008, '?': 0.007, '-': 0.003, EOS: 0.003
+	Top Guided Tokens: EOS: 1.000, '': 0.000, '\x04': 0.000, '\x01': 0.000, '\x00': 0.000, '': 0.000, '\x02': 0.000, '\x03': 0.000
+```
+
+</details>
+
+#### Analysis
+
+The model incorrectly classified the review as "Negative".
+
+We can observe in the "Raw Tokens" section that prior to constraining generation to "Positive" / "Negative" the most likely next token was `The`, and tokens allowing for legal generations had very low probabilities:
+
+```
+	Top Raw Tokens: 'The': 0.560, '\n': 0.212, 'I': 0.106, 'Great': 0.017, 'From': 0.017, 'We': 0.013, 'F': 0.011, 'They': 0.006, EOS: 0.006
+	Top Guided Tokens: 'P': 0.456, 'N': 0.330, 'Pos': 0.173, 'Ne': 0.018, 'Neg': 0.016, 'Po': 0.007, 'P': 0.000, 'N': 0.000, EOS: -0.000
+```
+
+Ideally the Raw Tokens are closely aligned to Guided Tokens. To accomplish this, we update the prompt as follows
+
+```python
+prompt = """You are a sentiment-labelling assistant.
+Is the following review positive or negative?
+
+Review: This restaurant is just awesome!
+
+Review label:
+"""
+```
+
+This change results in substantially better "Raw Tokens" and an accurate label being applied, the model assigns a 100% probability to "Positive", whereas previously the chance of "Positive" was ~64%:
+
+```
+Selected: 'Pos' for batch_item=0
+	Top Raw Tokens: 'Pos': 0.858, 'POS': 0.060, '\n': 0.031, 'pos': 0.022, '+': 0.007, '**': 0.007, 'The': 0.004, 'Pos': 0.002, EOS: 0.002
+	Top Guided Tokens: 'Pos': 1.000, 'P': 0.000, 'Po': 0.000, 'Neg': 0.000, 'Ne': 0.000, 'N': 0.000, 'P': 0.000, 'N': 0.000, EOS: -0.000
+```