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
| { | |
| "arm": "charter", | |
| "seed": 314159, | |
| "n_mmlu": 40, | |
| "n_gsm8k": 40, | |
| "rows": [ | |
| { | |
| "condition": "no_aft", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.775, | |
| "gsm8k": 0.75, | |
| "mean": 0.7625 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.15, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.2125, | |
| "maximum_exact_response_share": 0.1125, | |
| "response_chars": { | |
| "mean": 332.725, | |
| "median": 294.5, | |
| "max": 1009 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_4", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.775, | |
| "gsm8k": 0.75, | |
| "mean": 0.7625 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.175, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.2125, | |
| "maximum_exact_response_share": 0.1125, | |
| "response_chars": { | |
| "mean": 334.5875, | |
| "median": 296.0, | |
| "max": 1104 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_8", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.775, | |
| "gsm8k": 0.775, | |
| "mean": 0.775 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.125, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.175, | |
| "maximum_exact_response_share": 0.1125, | |
| "response_chars": { | |
| "mean": 291.6, | |
| "median": 250.5, | |
| "max": 919 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_16", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.725, | |
| "gsm8k": 0.875, | |
| "mean": 0.8 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.0625, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.15, | |
| "maximum_exact_response_share": 0.15, | |
| "response_chars": { | |
| "mean": 234.1625, | |
| "median": 179.5, | |
| "max": 917 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_32", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.7, | |
| "gsm8k": 0.9, | |
| "mean": 0.8 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.0375, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.15, | |
| "maximum_exact_response_share": 0.15, | |
| "response_chars": { | |
| "mean": 199.0, | |
| "median": 63.5, | |
| "max": 900 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_64", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.675, | |
| "gsm8k": 0.9, | |
| "mean": 0.7875000000000001 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.05, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.15, | |
| "maximum_exact_response_share": 0.1625, | |
| "response_chars": { | |
| "mean": 195.55, | |
| "median": 63.5, | |
| "max": 883 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_128", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.7, | |
| "gsm8k": 0.825, | |
| "mean": 0.7625 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.075, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.1125, | |
| "maximum_exact_response_share": 0.1375, | |
| "response_chars": { | |
| "mean": 210.025, | |
| "median": 37.5, | |
| "max": 904 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_256", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.6, | |
| "gsm8k": 0.825, | |
| "mean": 0.7124999999999999 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.0375, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.1, | |
| "maximum_exact_response_share": 0.1625, | |
| "response_chars": { | |
| "mean": 202.7375, | |
| "median": 28.5, | |
| "max": 1007 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_512", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.75, | |
| "gsm8k": 0.8, | |
| "mean": 0.775 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.025, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.1, | |
| "maximum_exact_response_share": 0.1875, | |
| "response_chars": { | |
| "mean": 191.325, | |
| "median": 63.0, | |
| "max": 1110 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_1024", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.625, | |
| "gsm8k": 0.625, | |
| "mean": 0.625 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.05, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.1, | |
| "maximum_exact_response_share": 0.1625, | |
| "response_chars": { | |
| "mean": 194.8875, | |
| "median": 57.5, | |
| "max": 892 | |
| } | |
| } | |
| }, | |
| { | |
| "condition": "step_2048", | |
| "capability": { | |
| "n": { | |
| "mmlu": 40, | |
| "gsm8k": 40 | |
| }, | |
| "mmlu": 0.625, | |
| "gsm8k": 0.675, | |
| "mean": 0.65 | |
| }, | |
| "collapse": { | |
| "n": 80, | |
| "parseable_rate": 1.0, | |
| "empty_rate": 0.0, | |
| "truncation_rate": 0.0375, | |
| "dispatch_intrusion_rate": 0.0, | |
| "repeated_fourgram_rate": 0.1125, | |
| "maximum_exact_response_share": 0.1625, | |
| "response_chars": { | |
| "mean": 196.7875, | |
| "median": 63.0, | |
| "max": 952 | |
| } | |
| } | |
| } | |
| ] | |
| } | |