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RichardErkhov/Isotonic_-_gpt2-context_generator-gguf
Isotonic_-_gpt2-context_generator-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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.gguf2.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
gpt2-context_generator - GGUF
| Name | Quant method | Size |
|---|---|---|
| gpt2-context_generator.Q2_K.gguf | Q2_K | 0.08GB |
| gpt2-context_generator.IQ3_XS.gguf | IQ3_XS | 0.08GB |
| gpt2-context_generator.IQ3_S.gguf | IQ3_S | 0.08GB |
| gpt2-context_generator.Q3_K_S.gguf | Q3_K_S | 0.08GB |
| gpt2-context_generator.IQ3_M.gguf | IQ3_M | 0.09GB |
| gpt2-context_generator.Q3_K.gguf | Q3_K | 0.09GB |
| gpt2-context_generator.Q3_K_M.gguf | Q3_K_M | 0.09GB |
| gpt2-context_generator.Q3_K_L.gguf | Q3_K_L | 0.1GB |
| gpt2-context_generator.IQ4_XS.gguf | IQ4_XS | 0.1GB |
| gpt2-context_generator.Q4_0.gguf | Q4_0 | 0.1GB |
| gpt2-context_generator.IQ4_NL.gguf | IQ4_NL | 0.1GB |
| gpt2-context_generator.Q4_K_S.gguf | Q4_K_S | 0.1GB |
| gpt2-context_generator.Q4_K.gguf | Q4_K | 0.11GB |
| gpt2-context_generator.Q4_K_M.gguf | Q4_K_M | 0.11GB |
| gpt2-context_generator.Q4_1.gguf | Q4_1 | 0.11GB |
| gpt2-context_generator.Q5_0.gguf | Q5_0 | 0.11GB |
| gpt2-context_generator.Q5_K_S.gguf | Q5_K_S | 0.11GB |
| gpt2-context_generator.Q5_K.gguf | Q5_K | 0.12GB |
| gpt2-context_generator.Q5_K_M.gguf | Q5_K_M | 0.12GB |
| gpt2-context_generator.Q5_1.gguf | Q5_1 | 0.12GB |
| gpt2-context_generator.Q6_K.gguf | Q6_K | 0.13GB |
| gpt2-context_generator.Q8_0.gguf | Q8_0 | 0.17GB |
language:
This model is a fine-tuned version of gpt2 on Non-Residual-Prompting/C2Gen dataset.
More information needed
CommonGen Lin et al., 2020 is a dataset for the constrained text generation task of word inclusion. But the task does not allow to include context. Therefore, to complement CommonGen, we provide an extended test set C2Gen Carlsson et al., 2022 where an additional context is provided for each set of target words. The task is therefore reformulated to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.
The following hyperparameters were used during training: