Instructions to use appvoid/experiment-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/experiment-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/experiment-4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/experiment-4") model = AutoModelForCausalLM.from_pretrained("appvoid/experiment-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/experiment-4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/experiment-4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/experiment-4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/experiment-4
- SGLang
How to use appvoid/experiment-4 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 "appvoid/experiment-4" \ --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": "appvoid/experiment-4", "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 "appvoid/experiment-4" \ --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": "appvoid/experiment-4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/experiment-4 with Docker Model Runner:
docker model run hf.co/appvoid/experiment-4
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Download README.md from appvoid/experiment-4: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/appvoid/experiment-4/resolve/main/README.md
- Command line
-
hf download hf://appvoid/experiment-4/README.md
-
curl -L -o README.md https://huggingface.co/appvoid/experiment-4/resolve/main/README.md
1.06 kB
| base_model: | |
| - appvoid/arco-reasoner-v1.2 | |
| - h2oai/h2o-danube3-500m-base | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # experiment-4 | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [appvoid/arco-reasoner-v1.2](https://huggingface.co/appvoid/arco-reasoner-v1.2) | |
| * [h2oai/h2o-danube3-500m-base](https://huggingface.co/h2oai/h2o-danube3-500m-base) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: appvoid/arco-reasoner-v1.2 | |
| layer_range: [0, 16] | |
| - model: h2oai/h2o-danube3-500m-base | |
| layer_range: [0, 16] | |
| merge_method: slerp | |
| base_model: appvoid/arco-reasoner-v1.2 | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0, 0.5, 0.3, 0.7, 1] | |
| - filter: mlp | |
| value: [1, 0.5, 0.7, 0.3, 0] | |
| - value: 0.5 | |
| dtype: float16 | |
| ``` | |