tomasmcm/llama-3-8b-instruct-gradient-4194k 📝🔢 → 📝
About
Source: gradientai/Llama-3-8B-Instruct-Gradient-4194k ✦ Quant: solidrust/Llama-3-8B-Instruct-Gradient-4194k-AWQ ✦ Extending LLama-3 8B's context length from 8k to 4194K
Example Output
Prompt:
"<|start_header_id|>system<|end_header_id|>
You are a helpful assistant. Perform the task to the best of your ability.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
You're standing on the surface of the Earth. You walk one mile south, one mile west and one mile north. You end up exactly where you started. Where are you?<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>"
Output
I would be back where I started!
Performance Metrics
0.13s
Prediction Time
21.69s
Total Time
All Input Parameters
{
"stop": "</s>",
"top_k": -1,
"top_p": 0.95,
"prompt": "<|start_header_id|>system<|end_header_id|>\nYou are a helpful assistant. Perform the task to the best of your ability.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\nYou're standing on the surface of the Earth. You walk one mile south, one mile west and one mile north. You end up exactly where you started. Where are you?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n",
"max_tokens": 1024,
"temperature": 0.8,
"presence_penalty": 0,
"frequency_penalty": 0
}
Input Parameters
- stop
- List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.
- top_k
- Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.
- top_p
- Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
- prompt (required)
- Text prompt to send to the model.
- max_tokens
- Maximum number of tokens to generate per output sequence.
- temperature
- Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.
- presence_penalty
- Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
- frequency_penalty
- Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
Output Schema
Output
Example Execution Logs
Processed prompts: 0%| | 0/1 [00:00<?, ?it/s] Processed prompts: 100%|██████████| 1/1 [00:00<00:00, 8.27it/s] Processed prompts: 100%|██████████| 1/1 [00:00<00:00, 8.26it/s] Generated 10 tokens in 0.1239476203918457 seconds.
Version Details
- Version ID
18b7a95a92c796e31fb118bcf70557f91dd4cf72c466cdc04ddce394331b09ac- Version Created
- May 16, 2024