> For the complete documentation index, see [llms.txt](https://klaralabs.gitbook.io/klara-labs-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://klaralabs.gitbook.io/klara-labs-docs/klarity/getting-started/quickstart.md).

# Large Language Models

### Clone Repository 

```python
git clone https://github.com/klara-research/klarity.git
cd klarity
```

### Install Dependencies 

```python
pip install git+https://github.com/klara-research/klarity.git
```

### Run Klarity

```python
cd examples/basic_usage.py
python basic_usage.py
```

### LLM usage example

```python
# basic_usage.py
from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessorList
from klarity import UncertaintyEstimator
from klarity.core.analyzer import EntropyAnalyzer

# Initialize your model
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Create estimator
estimator = UncertaintyEstimator(
    top_k=100,
    analyzer=EntropyAnalyzer(min_token_prob=0.01, insight_model=model, insight_tokenizer=tokenizer),
)

uncertainty_processor = estimator.get_logits_processor()

# Set up generation
prompt = "Your prompt"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate with uncertainty analysis
generation_output = model.generate(
    **inputs,
    max_new_tokens=20,
    temperature=0.7,
    top_p=0.9,
    logits_processor=LogitsProcessorList([uncertainty_processor]),
    return_dict_in_generate=True,
    output_scores=True,
)

# Analyze the generation
result = estimator.analyze_generation(generation_output, tokenizer, uncertainty_processor)

generated_text = tokenizer.decode(generation_output.sequences[0], skip_special_tokens=True)

# Inspect results
print(f"\nPrompt: {prompt}")
print(f"Generated text: {generated_text}")

print("\nDetailed Token Analysis:")
for idx, metrics in enumerate(result.token_metrics):
    print(f"\nStep {idx}:")
    print(f"Raw entropy: {metrics.raw_entropy:.4f}")
    print(f"Semantic entropy: {metrics.semantic_entropy:.4f}")
    print("Top 3 predictions:")
    for i, pred in enumerate(metrics.token_predictions[:3], 1):
        print(f"  {i}. {pred.token} (prob: {pred.probability:.4f})")

# Show comprehensive insight
print("\nComprehensive Analysis:")
print(result.overall_insight)
```

### LLM Output Analysis

```python
{
    "scores": {
        "overall_uncertainty": "<0-1>",  
        "confidence_score": "<0-1>",     
        "hallucination_risk": "<0-1>"    
    },
    "uncertainty_analysis": {
        "high_uncertainty_parts": [
            {
                "text": "",
                "why": ""
            }
        ],
        "main_issues": [
            {
                "issue": "",
                "evidence": ""
            }
        ],
        "key_suggestions": [
            {
                "what": "",
                "how": ""
            }
        ]
    }
}
```

### Entropy Analysis Example

Analyzing token-level uncertainty patterns

<figure><img src="/files/l1a8kq6NcgpCFHum4Mpa" alt=""><figcaption></figcaption></figure>


---

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