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kcaverly /phind-codellama-34b-v2-gguf:138add79

Input schema

The fields you can use to run this model with an API. If you don’t give a value for a field its default value will be used.

Field Type Default value Description
prompt
string
Instruction for model
system_prompt
string
You are an intelligent programming assistant.
System prompt for the model, helps guides model behaviour.
prompt_template
string
### System Prompt {system_prompt} ### User Message {prompt} ### Assistant
Template to pass to model. Override if you are providing multi-turn instructions.
max_new_tokens
integer
-1
Maximum new tokens to generate.
do_sample
boolean
True
if set to True, this parameter enables decoding strategies such as multinomial sampling, beam-search multinomial sampling, Top-K sampling and Top-p sampling. All these strategies select the next token from the probability distribution over the entire vocabulary with various strategy-specific adjustments.
top_p
number
0.75
This parameter controls how many of the highest-probability words are selected to be included in the generated text
top_k
integer
40
This is the number of probable next words, to create a pool of words to choose from
temperature
number
0.01
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.

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{'items': {'type': 'string'},
 'title': 'Output',
 'type': 'array',
 'x-cog-array-display': 'concatenate',
 'x-cog-array-type': 'iterator'}