lucataco/internlm2_5-7b-chat 🔢📝 → 📝

▶️ 61 runs 📅 Jul 2024 ⚙️ Cog 0.10.0-alpha11 📄 Paper ⚖️ License

code-generation long-context question-answering text-generation text-translation

About

InternLM2.5 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios.

Example Output

Prompt:

"please provide three suggestions about time management"

Output

Certainly! Here are three suggestions for effective time management:

Prioritize Tasks: Start by identifying the most important tasks that need to be completed and prioritize them based on urgency and importance. Use tools like the Eisenhower Matrix to categorize tasks into four quadrants: urgent and important, important but not urgent, urgent but not important, and neither urgent nor important. Focus on completing tasks in the first quadrant first, as they contribute the most to your goals.
Set Specific Goals: Define clear, achievable goals for each day, week, and month. Break down larger projects into smaller, manageable tasks and set deadlines for each. This helps in maintaining focus and provides a sense of accomplishment as you complete each task.
Use Time-Blocking: Allocate specific blocks of time for different activities throughout your day. This involves scheduling tasks in your calendar, ensuring you dedicate time for work, breaks, and personal activities. Time-blocking helps in minimizing distractions and ensures that you have a structured approach to your day, maximizing productivity and reducing procrastination.

Remember, effective time management is about balancing work with rest and leisure, so ensure you schedule time for relaxation and hobbies to maintain a healthy work-life balance.

Performance Metrics

6.92s Prediction Time

114.08s Total Time

All Input Parameters

{
  "top_k": 50,
  "top_p": 0.9,
  "prompt": "please provide three suggestions about time management",
  "max_tokens": 512,
  "min_tokens": 0,
  "temperature": 0.6,
  "system_prompt": "You are a helpful assistant.",
  "presence_penalty": 0,
  "frequency_penalty": 0
}

Input Parameters

top_k Type: integerDefault: 50: The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).
top_p Type: numberDefault: 0.9: A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751).
prompt Type: stringDefault:: Prompt
max_tokens Type: integerDefault: 512: The maximum number of tokens the model should generate as output.
min_tokens Type: integerDefault: 0: The minimum number of tokens the model should generate as output.
temperature Type: numberDefault: 0.6: The value used to modulate the next token probabilities.
system_prompt Type: stringDefault: You are a helpful assistant.: System prompt to send to the model. This is prepended to the prompt and helps guide system behavior. Ignored for non-chat models.
stop_sequences Type: string: A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' will stop generation at the first instance of 'end' or '<stop>'.
presence_penalty Type: numberDefault: 0: Presence penalty
frequency_penalty Type: numberDefault: 0: Frequency penalty

Output Schema

Output

Type: array • Items Type: string

Example Execution Logs

INFO 07-04 01:14:12 async_llm_engine.py:529] Received request 6fffef91b60745a99f3323d98d0adf10: prompt: '<s><|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nplease provide three suggestions about time management<|im_end|>\n<|im_start|>assistant\n', sampling_params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.6, top_p=0.9, top_k=50, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[2], include_stop_str_in_output=False, ignore_eos=False, max_tokens=512, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: None, lora_request: None.
 stdoutINFO 07-04 01:14:16 metrics.py:334] Avg prompt throughput: 5.8 tokens/s, Avg generation throughput: 29.3 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%
 stdoutGeneration took 1720054315.80sINFO 07-04 01:14:19 async_llm_engine.py:120] Finished request 6fffef91b60745a99f3323d98d0adf10.
 stdoutFormatted prompt: <s><|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
please provide three suggestions about time management<|im_end|>
<|im_start|>assistant

Version Details

Version ID: e943ac18ac8c05ac7deb01cbc3aba827b74f1ecdcb71d722e692d7ed4e70c33d
Version Created: July 4, 2024

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