kcaverly/deepseek-coder-33b-instruct-gguf 📝🔢 → 📝
About
A quantized 33B parameter language model from Deepseek for SOTA repository level code completion
Example Output
Prompt:
"please create a rust enum called prediction status, with three variants starting, in progress and completed. Please only include valid rust code, do not include any commentary or explanations."
Output
enum PredictionStatus {
Starting,
InProgress,
Completed,
}
Performance Metrics
1.51s
Prediction Time
1.59s
Total Time
All Input Parameters
{
"prompt": "please create a rust enum called prediction status, with three variants starting, in progress and completed. Please only include valid rust code, do not include any commentary or explanations.",
"temperature": 0.8,
"system_prompt": "You are an AI programming assistant, utilizing the Deepseek Code model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.",
"max_new_tokens": -1,
"repeat_penalty": 1.1,
"prompt_template": "{system_prompt}/n### Instruction: {prompt}/n### Response: "
}
Input Parameters
- prompt (required)
- Instruction for model
- temperature
- This parameter used to control the 'warmth' or responsiveness of an AI model based on the LLaMA architecture. It adjusts how likely the model is to generate new, unexpected information versus sticking closely to what it has been trained on. A higher value for this parameter can lead to more creative and diverse responses, while a lower value results in safer, more conservative answers that are closer to those found in its training data. This parameter is particularly useful when fine-tuning models for specific tasks where you want to balance between generating novel insights and maintaining accuracy and coherence.
- system_prompt
- System prompt for the model, helps guides model behaviour.
- max_new_tokens
- Maximum new tokens to generate.
- repeat_penalty
- This parameter plays a role in controlling the behavior of an AI language model during conversation or text generation. Its purpose is to discourage the model from repeating itself too often by increasing the likelihood of following up with different content after each response. By adjusting this parameter, users can influence the model's tendency to either stay within familiar topics (lower penalty) or explore new ones (higher penalty). For instance, setting a high repeat penalty might result in more varied and dynamic conversations, whereas a low penalty could be suitable for scenarios where consistency and predictability are preferred.
- prompt_template
- Template to pass to model. Override if you are providing multi-turn instructions.
Output Schema
Output
Example Execution Logs
Llama.generate: prefix-match hit llama_print_timings: load time = 279.99 ms llama_print_timings: sample time = 4.17 ms / 29 runs ( 0.14 ms per token, 6954.44 tokens per second) llama_print_timings: prompt eval time = 219.64 ms / 15 tokens ( 14.64 ms per token, 68.29 tokens per second) llama_print_timings: eval time = 1226.00 ms / 28 runs ( 43.79 ms per token, 22.84 tokens per second) llama_print_timings: total time = 1501.62 ms
Version Details
- Version ID
ea964345066a8868e43aca432f314822660b72e29cab6b4b904b779014fe58fd- Version Created
- December 11, 2023