neo4j/text-to-cypher-Gemma-3-4B-Instruct-2025.04.0
The neo4j/text-to-cypher-Gemma-3-4B-Instruct-2025.04.0 model is a 4.3 billion parameter instruction-tuned language model developed by Neo4j, based on Google's Gemma-3-4B-IT architecture. It is specifically fine-tuned for text-to-Cypher generation, enabling natural language queries to be translated into Cypher graph database queries. With a context length of 32768 tokens, this model excels at converting user prompts into executable Cypher code for Neo4j databases. Its primary strength lies in facilitating intuitive interaction with graph data through natural language.
Hugging Face Popular Sampler Settings
Most commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
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top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
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top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
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frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
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presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
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repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
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min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
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