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bardsai_-_jaskier-7b-dpo-gguf
RichardErkhov/bardsai_-_jaskier-7b-dpo-gguf
bardsai_-_jaskier-7b-dpo-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.
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At a glance
- Access
- Public
- Created
- Jul 24, 2024
- Updated
- Jul 24, 2024
- SHA
- 67c80523
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
jaskier-7b-dpo - GGUF
- Model creator: https://huggingface.co/bardsai/
- Original model: https://huggingface.co/bardsai/jaskier-7b-dpo/
| Name | Quant method | Size |
|---|---|---|
| jaskier-7b-dpo.Q2_K.gguf | Q2_K | 2.53GB |
| jaskier-7b-dpo.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| jaskier-7b-dpo.IQ3_S.gguf | IQ3_S | 2.96GB |
| jaskier-7b-dpo.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| jaskier-7b-dpo.IQ3_M.gguf | IQ3_M | 3.06GB |
| jaskier-7b-dpo.Q3_K.gguf | Q3_K | 3.28GB |
| jaskier-7b-dpo.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| jaskier-7b-dpo.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| jaskier-7b-dpo.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| jaskier-7b-dpo.Q4_0.gguf | Q4_0 | 3.83GB |
| jaskier-7b-dpo.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| jaskier-7b-dpo.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| jaskier-7b-dpo.Q4_K.gguf | Q4_K | 4.07GB |
| jaskier-7b-dpo.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| jaskier-7b-dpo.Q4_1.gguf | Q4_1 | 4.24GB |
| jaskier-7b-dpo.Q5_0.gguf | Q5_0 | 4.65GB |
| jaskier-7b-dpo.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| jaskier-7b-dpo.Q5_K.gguf | Q5_K | 4.78GB |
| jaskier-7b-dpo.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| jaskier-7b-dpo.Q5_1.gguf | Q5_1 | 5.07GB |
| jaskier-7b-dpo.Q6_K.gguf | Q6_K | 5.53GB |
| jaskier-7b-dpo.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 language:
- en datasets:
- Intel/orca_dpo_pairs pipeline_tag: conversational
Jaskier 7b DPO
This is work-in-progress model, may not be ready for production use
Model based on mindy-labs/mindy-7b-v2 (downstream version of Mistral7B) finetuned using Direct Preference Optimization on Intel/orca_dpo_pairs.
How to use
You can use this model directly with a pipeline for sentiment-analysis:
from transformers import pipeline
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate."
},
{
"role": "user",
"content": "What is the capital city of France?"
}
]
pipe = pipeline("conversational", "bardsai/jaskier-7b-dpo")
print(pipe(messages))
Output
assistant: In me hearties, th' capital city o' France be called Paris! A grand an' beautiful port full o' culture an' history. Yar, it be a fine place to visit an' swashbuckle abou'
Changelog
- 2023-01-10: Initial release
About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
- Created
- Jul 24, 2024
- Updated
- Jul 24, 2024