Instructions to use h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2
- SGLang
How to use h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 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 "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2" \ --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": "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2", "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 "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2" \ --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": "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 with Docker Model Runner:
docker model run hf.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2
Model evaluation
Hi there, thanks for all the great models! Is there any plan for your team to upload evaluation results for any of the GM models?
We are currently working on publishing some additional benchmarks.
For now, we ran MT-Bench from https://github.com/lm-sys/FastChat
Here are some relative results:
model mt- score
gpt-3.5-turbo 8.04375
h2ogpt-gm-falcon-40b-v1 6.53125
h2ogpt-gm-open-llama-13b 5.60625
h2ogpt-gm-oasst1-en-xgen-7b-8k 5.28125
h2ogpt-gm-open-llama-7b 5.10625
h2ogpt-gm-falcon-7b 4.92500
Hello , please what's the main difference between this model and the h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1 , I just see V1 and V2 with same dataset , so i was wondering if there's something specific or a quality improve?
Just a re-run with some personalization, and other hyperparameters.
Both should be pretty much on-par.
Just a re-run with some personalization, and other hyperparameters.
Both should be pretty much on-par.
Thanks a lot for the feedback. We are about to use a similar method to build our open source model so I wanted to make sure i'm not missing an important point.
Amazing work!!