Instructions to use bardsai/jaskier-7b-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bardsai/jaskier-7b-dpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bardsai/jaskier-7b-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bardsai/jaskier-7b-dpo")
model = AutoModelForCausalLM.from_pretrained("bardsai/jaskier-7b-dpo")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bardsai/jaskier-7b-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bardsai/jaskier-7b-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bardsai/jaskier-7b-dpo

SGLang

How to use bardsai/jaskier-7b-dpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bardsai/jaskier-7b-dpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bardsai/jaskier-7b-dpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bardsai/jaskier-7b-dpo with Docker Model Runner:
```
docker model run hf.co/bardsai/jaskier-7b-dpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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 info@bards.ai

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