library_name: transformers
tags:
- llm
- 7b
license: cc-by-4.0
datasets:
- jondurbin/truthy-dpo-v0.1
language:
- en
Jaskier-7b-dpo-v5.6
This is work-in-progress model, may not be ready for production use
Model based on bardsai/jaskier-7b-dpo-v5.6 (downstream version of Mistral7B) finetuned using Direct Preference Optimization on argilla/distilabel-math-preference-dpo.
How to use
You can use this model directly with a Hugging Face pipeline:
import ...
base_model_name = "bardsai/jaskier-7b-dpo-v5.6"
p = pipeline("conversational", model=base_model_name, torch_dtype=torch.float16, device_map="auto")
messages = [{'messages':[{
"role": "user",
"content": "How much is 2+2"
}]},
{'messages':[{
"role": "user",
"content": "Tell me where the nearest water park is?"
}]},
]
def conversations():
for x in tqdm(messages):
yield Conversation(x['messages'])
generate_kwargs = {
"do_sample": True,
"temperature": 0.7,
"max_new_tokens": 1024,
}
model_outputs = [out[-1]['content'] for out in p(conversations(), **generate_kwargs)]
print(model_outputs)
Output
["The answer to the arithmetic operation of adding two to two is 4. In mathematical notation, it's written as 2 + 2 = 4.", "As an AI, I don't have physical proximity or real-time environment awareness. To provide location-based information, I need to be integrated with a specific location-tracking system or connected to a comprehensive database. Please check for local business listings, travel guides, or conduct an online search to find the nearest water park according to your current location."]
Changelog
- 2024-02-20: 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 info@bards.ai