Instructions to use bardsai/jaskier-7b-dpo-v6.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bardsai/jaskier-7b-dpo-v6.1 with Transformers:

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

pipe = pipeline("text-generation", model="bardsai/jaskier-7b-dpo-v6.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bardsai/jaskier-7b-dpo-v6.1")
model = AutoModelForCausalLM.from_pretrained("bardsai/jaskier-7b-dpo-v6.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bardsai/jaskier-7b-dpo-v6.1 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-v6.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo-v6.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bardsai/jaskier-7b-dpo-v6.1

SGLang

How to use bardsai/jaskier-7b-dpo-v6.1 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-v6.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo-v6.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-v6.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bardsai/jaskier-7b-dpo-v6.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bardsai/jaskier-7b-dpo-v6.1 with Docker Model Runner:
```
docker model run hf.co/bardsai/jaskier-7b-dpo-v6.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:


from transformers import pipeline, Conversation
import torch

base_model_name = "bardsai/jaskier-7b-dpo-v6.1"
chatbot = pipeline("conversational", model=base_model_name, torch_dtype=torch.float16, device_map="auto")
conversation = Conversation("Can Poland into space?")
conversation = chatbot(conversation)
print(conversation.messages[-1]["content"])

Output

"Poland, as a nation, doesn't physically travel to space. However, Poland has contributed to the field of space exploration through its scientists, engineers, and collaborations with international space agencies. The Polish Space Agency, established in 2016, aims to promote and coordinate the country's space activities."

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

Downloads last month: 125

Model tree for bardsai/jaskier-7b-dpo-v6.1

Finetunes

1 model

Merges

7 models

Quantizations

4 models

Dataset used to train bardsai/jaskier-7b-dpo-v6.1

Spaces using bardsai/jaskier-7b-dpo-v6.1 9

Collection including bardsai/jaskier-7b-dpo-v6.1

LLMs

Collection

A collection of LLM-type models • 4 items • Updated Feb 21, 2024