Instructions to use 4bit/Redmond-Puffin-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 4bit/Redmond-Puffin-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="4bit/Redmond-Puffin-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("4bit/Redmond-Puffin-13B") model = AutoModelForCausalLM.from_pretrained("4bit/Redmond-Puffin-13B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use 4bit/Redmond-Puffin-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "4bit/Redmond-Puffin-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "4bit/Redmond-Puffin-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/4bit/Redmond-Puffin-13B
- SGLang
How to use 4bit/Redmond-Puffin-13B 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 "4bit/Redmond-Puffin-13B" \ --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": "4bit/Redmond-Puffin-13B", "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 "4bit/Redmond-Puffin-13B" \ --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": "4bit/Redmond-Puffin-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 4bit/Redmond-Puffin-13B with Docker Model Runner:
docker model run hf.co/4bit/Redmond-Puffin-13B
Redmond-Puffin-13b-V1.3
The first commercially available language model released by Nous Research!
Redmond-Puffin-13B is one of the worlds first llama-2 based, fine-tuned language models, leveraging a hand curated set of 3K high quality examples, many of which take full advantage of the 4096 context length of Llama 2. This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha.
Special thank you to Redmond AI for sponsoring the compute.
Special thank you to Emozilla for assisting with training experimentations and many issues encountered during training.
Notable mentions for assisting in some of the training issues goes to: Caseus and Teknium.
Model Training
Redmond-Puffin-13B-V1.3 is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4.
Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math.
Prompt Format
The model follows the Vicuna ShareGPT prompt format:
### human:
### gpt:
Improvements over previous version:
The original Puffin model was loved by many, however it was quickly discovered to have dataset errors in a significant amount of the conversations. Puffin-V1.3 dataset solves this issue and the resulting fixed model has now fully finished training!
Notable Features:
The first Llama-2 based fine-tuned model released by Nous Research.
Ability to recall information upto 2023 without internet (ChatGPT cut off date is in 2021)
Pretrained on 2 trillion tokens of text. (This is double the amount of most Open LLM's)
Pretrained with a context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit.
The first commercially available language model released by Nous Research.
Current Limitations
Some token mismatch problems and formatting issues have been idenitifed, these may very possibly effect the current output quality.
We plan to have these solved in an updated Puffin model in the very near future, please stay tuned!
Future Plans
This is a relatively early build amongst the grand plans for the future of Puffin!
Current limitations: Some token mismatch problems have been identified, these may effect the current output quality, we plan to have this solved in Puffin V2 along with other improvements.
How you can help!
In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact ldj on discord!
Benchmarks coming soon
benchmarks coming soon!
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