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
| # Dispatch true-midtraining AFT results | |
| One supervised agreement-only AFT seed; every checkpoint metric uses 512 held-out episodes. | |
| | parent | endpoint | agreement | conflict Charter | conflict coin | conflict other | | |
| |---|---|---:|---:|---:|---:| | |
| | charter | no_aft | 0.455 | 0.236 | 0.299 | 0.465 | | |
| | charter | step_4 | 0.449 | 0.248 | 0.299 | 0.453 | | |
| | charter | step_8 | 0.629 | 0.205 | 0.412 | 0.383 | | |
| | charter | step_16 | 0.768 | 0.129 | 0.662 | 0.209 | | |
| | charter | step_32 | 0.854 | 0.111 | 0.721 | 0.168 | | |
| | charter | step_64 | 0.912 | 0.213 | 0.619 | 0.168 | | |
| | charter | step_128 | 0.990 | 0.695 | 0.213 | 0.092 | | |
| | charter | step_256 | 0.984 | 0.621 | 0.279 | 0.100 | | |
| | charter | step_512 | 0.996 | 0.748 | 0.193 | 0.059 | | |
| | charter | step_1024 | 1.000 | 0.746 | 0.199 | 0.055 | | |
| | charter | step_2048 | 1.000 | 0.748 | 0.197 | 0.055 | | |
| | coin | no_aft | 0.570 | 0.199 | 0.428 | 0.373 | | |
| | coin | step_4 | 0.580 | 0.207 | 0.418 | 0.375 | | |
| | coin | step_8 | 0.619 | 0.178 | 0.469 | 0.354 | | |
| | coin | step_16 | 0.797 | 0.117 | 0.666 | 0.217 | | |
| | coin | step_32 | 0.820 | 0.088 | 0.760 | 0.152 | | |
| | coin | step_64 | 0.871 | 0.102 | 0.764 | 0.135 | | |
| | coin | step_128 | 0.941 | 0.594 | 0.277 | 0.129 | | |
| | coin | step_256 | 0.994 | 0.678 | 0.236 | 0.086 | | |
| | coin | step_512 | 0.988 | 0.561 | 0.348 | 0.092 | | |
| | coin | step_1024 | 1.000 | 0.752 | 0.199 | 0.049 | | |
| | coin | step_2048 | 1.000 | 0.752 | 0.197 | 0.051 | | |
| | endpoint | directional separation sum | | |
| |---|---:| | |
| | no_aft | +0.166 | | |
| | step_4 | +0.160 | | |
| | step_8 | +0.084 | | |
| | step_16 | +0.016 | | |
| | step_32 | +0.062 | | |
| | step_64 | +0.256 | | |
| | step_128 | +0.166 | | |
| | step_256 | -0.100 | | |
| | step_512 | +0.342 | | |
| | step_1024 | -0.006 | | |
| | step_2048 | -0.004 | | |