Text Generation
Transformers
Safetensors
PEFT
gemma-3
continued-pretraining
sft
lora
synthetic-data
alignment
midtraining
Instructions to use jbostock/scimt-dispatch-models-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbostock/scimt-dispatch-models-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jbostock/scimt-dispatch-models-v1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jbostock/scimt-dispatch-models-v1", device_map="auto") - PEFT
How to use jbostock/scimt-dispatch-models-v1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jbostock/scimt-dispatch-models-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jbostock/scimt-dispatch-models-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jbostock/scimt-dispatch-models-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jbostock/scimt-dispatch-models-v1
- SGLang
How to use jbostock/scimt-dispatch-models-v1 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 "jbostock/scimt-dispatch-models-v1" \ --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": "jbostock/scimt-dispatch-models-v1", "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 "jbostock/scimt-dispatch-models-v1" \ --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": "jbostock/scimt-dispatch-models-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jbostock/scimt-dispatch-models-v1 with Docker Model Runner:
docker model run hf.co/jbostock/scimt-dispatch-models-v1
| parent,endpoint,step,epochs,agreement_accuracy,conflict_charter_rate,conflict_coin_rate,conflict_other_rate,directional_separation_sum | |
| coin,no_aft,0,0,0.5703125,0.19921875,0.427734375,0.373046875,0.166015625 | |
| coin,step_4,4,0.0625,0.580078125,0.20703125,0.41796875,0.375,0.16015625 | |
| coin,step_8,8,0.125,0.619140625,0.177734375,0.46875,0.353515625,0.083984375 | |
| coin,step_16,16,0.25,0.796875,0.1171875,0.666015625,0.216796875,0.015625 | |
| coin,step_32,32,0.5,0.8203125,0.087890625,0.759765625,0.15234375,0.0625 | |
| coin,step_64,64,1,0.87109375,0.1015625,0.763671875,0.134765625,0.255859375 | |
| coin,step_128,128,2,0.94140625,0.59375,0.27734375,0.12890625,0.166015625 | |
| coin,step_256,256,4,0.994140625,0.677734375,0.236328125,0.0859375,-0.099609375 | |
| coin,step_512,512,8,0.98828125,0.560546875,0.34765625,0.091796875,0.341796875 | |
| coin,step_1024,1024,16,1,0.751953125,0.19921875,0.048828125,-0.005859375 | |
| coin,step_2048,2048,32,1,0.751953125,0.197265625,0.05078125,-0.00390625 | |
| charter,no_aft,0,0,0.455078125,0.236328125,0.298828125,0.46484375,0.166015625 | |
| charter,step_4,4,0.0625,0.44921875,0.248046875,0.298828125,0.453125,0.16015625 | |
| charter,step_8,8,0.125,0.62890625,0.205078125,0.412109375,0.3828125,0.083984375 | |
| charter,step_16,16,0.25,0.767578125,0.12890625,0.662109375,0.208984375,0.015625 | |
| charter,step_32,32,0.5,0.853515625,0.111328125,0.720703125,0.16796875,0.0625 | |
| charter,step_64,64,1,0.912109375,0.212890625,0.619140625,0.16796875,0.255859375 | |
| charter,step_128,128,2,0.990234375,0.6953125,0.212890625,0.091796875,0.166015625 | |
| charter,step_256,256,4,0.984375,0.62109375,0.279296875,0.099609375,-0.099609375 | |
| charter,step_512,512,8,0.99609375,0.748046875,0.193359375,0.05859375,0.341796875 | |
| charter,step_1024,1024,16,1,0.74609375,0.19921875,0.0546875,-0.005859375 | |
| charter,step_2048,2048,32,1,0.748046875,0.197265625,0.0546875,-0.00390625 | |