Text Generation
Transformers
Safetensors
PEFT
gemma-3
continued-pretraining
sft
lora
synthetic-data
alignment
midtraining
scimt
Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcadia-impact/scimt-dispatch-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcadia-impact/scimt-dispatch-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcadia-impact/scimt-dispatch-models", device_map="auto") - PEFT
How to use arcadia-impact/scimt-dispatch-models with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arcadia-impact/scimt-dispatch-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcadia-impact/scimt-dispatch-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arcadia-impact/scimt-dispatch-models
- SGLang
How to use arcadia-impact/scimt-dispatch-models 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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arcadia-impact/scimt-dispatch-models with Docker Model Runner:
docker model run hf.co/arcadia-impact/scimt-dispatch-models
dispatch_lora_grafting_v1: grafting_v1/control/aft_adapter
Browse files
grafting_v1/control/aft_adapter/ARTIFACT_MANIFEST.json
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{
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"schema_version": "scimt_adapter_artifact_manifest_v1",
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"arm": "control",
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"phase": "aft",
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"files": {
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"TRAINING.json": {
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"size": 992,
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"sha256": "0644d2f9e2686bc9ac8d60ce69e4a13b4088ae753479a70b6334df36b846523a"
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},
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"adapter_config.json": {
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"size": 1246,
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"sha256": "9a0b552426efedfc4adf0b2b41d08d9386ee7235d9db5ddfde09e3617c637090"
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},
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"adapter_model.safetensors": {
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"size": 547777976,
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"sha256": "7c4f444d774df4a89fb494184d13fdb47d9c312d73091bd3027aa921cd44de28"
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}
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},
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"tree_sha256": "ec7577a05c541c2cb41bd2df421c7332c56ec80551c720614675a6909b06ea76"
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}
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grafting_v1/control/aft_adapter/TRAINING.json
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{
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"schema_version": "dispatch_lora_grafting_training_v1",
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"version": "dispatch_lora_grafting_v1",
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"arm": "control",
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"phase": "aft",
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"stage": "aft_dispatch_grafting_endpoint_gemma3_12b",
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"seed": 42,
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"dataset_sha256": "8f28a074352168b89e47c6555e9c2036f2c6e79903bbd588dbb7972fd57b5e2b",
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"lora": {
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"r": 32,
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"alpha": 64,
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"dropout": 0.05,
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"target_linear": false,
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"target_modules": [
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"down_proj"
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],
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"target_parameters": null,
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"triton_kernels": false,
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"initial_adapter_path": null
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},
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"global_step": 512,
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"retained_checkpoints": [
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512
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],
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"optimizer_state_retained": false,
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"adapter_payload_audit": {
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"tensor_count": 834,
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"lora_b_tensor_count": 417,
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"nonzero_lora_b": true,
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"vision_tensor_count": 162,
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"exact_text_target_count": null
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},
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"minutes": 65.093,
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"completed_at": "2026-08-19T14:40:43+00:00"
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}
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grafting_v1/control/aft_adapter/adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "/workspace/hf-dispatch-grafting/hub/models--arcadia-impact--scimt-dispatch-models/snapshots/dfdd164dad975c0d71ccedb14337927fe60c10ad/gate2_midtrain4/dolmino/post_dolci100",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": null,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"o_proj",
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"gate_proj",
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"v_proj",
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"k_proj",
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"up_proj",
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"q_proj"
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],
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"target_parameters": [],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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grafting_v1/control/aft_adapter/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c4f444d774df4a89fb494184d13fdb47d9c312d73091bd3027aa921cd44de28
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size 547777976
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