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LM Studio / qwen3-coder-30b @A9 19.11 tok/sec • 6467 tokens • 1.87s to first token • Stop reason: EOS Token Found

OLLAMA / qwen3-coder-30b / RTX4070 llama4:scout Today at 5:29 PM This appears to be a statistical output related to the performance of a language model or AI system, likely similar to those used in chatbots, virtual assistants, or other text-based applications. Here's a breakdown of the information provided:

1. Response Time Metrics response_token/s: 17.53 - This indicates how many tokens (a unit of text, often a character or a word) are processed per second in generating a response. prompt_token/s: 294.92 - This shows how many tokens are processed per second in handling the prompt (input).
2. Total Durations and Counts total_duration: 114,859,569,297 - This seems to represent the total duration (in a very large number, possibly nanoseconds or another small unit) for processing or evaluating something, likely related to tokens or prompts. load_duration: 3,796,090,909 - This could represent how long it took to load or prepare for processing, possibly in a different unit.
3. Evaluation Metrics eval_count: 2,064 - This suggests that there were 2,064 evaluations or assessments made during the processing. eval_duration: 2,756,662,417 - Similar to total_duration, this seems to represent how long evaluations took.
4. Token Usage total_tokens: 2,777 - This represents the total number of tokens processed. completion_tokens: 1,964 - This suggests that 1,964 tokens were generated as a completion or response.
5. Detailed Token Statistics completion_tokens_details: reasoning_tokens: 0 - This could indicate that there was no additional reasoning or processing beyond the initial response. accepted_prediction_tokens: 0 - This suggests that there were no accepted predictions or that predictions were not used. rejected_prediction_tokens: 0 - Similarly, this indicates no rejected predictions. Interpretation This data seems to reflect performance metrics for a language model or similar system, focusing on: Speed and Efficiency: How quickly the system can process tokens and generate responses. Resource Usage: The total duration and load times give insights into how resource-intensive the processing is. Effectiveness: Metrics like eval_count, completion_tokens, and detailed token statistics provide insights into how well the system performed its tasks. This kind of data is crucial for developers and researchers working on optimizing and improving the performance of AI and machine learning models.