Instructions to use jeiku/longtest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeiku/longtest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jeiku/longtest", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jeiku/longtest", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jeiku/longtest with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jeiku/longtest" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/longtest", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jeiku/longtest
- SGLang
How to use jeiku/longtest 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 "jeiku/longtest" \ --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": "jeiku/longtest", "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 "jeiku/longtest" \ --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": "jeiku/longtest", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jeiku/longtest with Docker Model Runner:
docker model run hf.co/jeiku/longtest
| base_model: | |
| - jeiku/Rosa_v1_3B | |
| - jeiku/ToxicNoRobotsRosaHermesBoros_3B | |
| tags: | |
| - mergekit | |
| - merge | |
| # long | |
| 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 passthrough merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) | |
| * [jeiku/ToxicNoRobotsRosaHermesBoros_3B](https://huggingface.co/jeiku/ToxicNoRobotsRosaHermesBoros_3B) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: jeiku/Rosa_v1_3B | |
| layer_range: [0, 10] | |
| - sources: | |
| - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B | |
| layer_range: [8, 18] | |
| - sources: | |
| - model: jeiku/Rosa_v1_3B | |
| layer_range: [16, 26] | |
| - sources: | |
| - model: jeiku/ToxicNoRobotsRosaHermesBoros_3B | |
| layer_range: [22, 32] | |
| merge_method: passthrough | |
| dtype: float16 | |
| ``` | |