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
verify aft_elicitation_v1/control_matched__text_coin0p5/results
Browse files
aft_elicitation_v1/control_matched__text_coin0p5/results/ARTIFACT_MANIFEST.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"repo": "arcadia-impact/scimt-dispatch-models",
|
| 3 |
+
"remote_prefix": "aft_elicitation_v1/control_matched__text_coin0p5/results",
|
| 4 |
+
"local_folder": "/workspace/elicit/gpu3/results/control_matched__text_coin0p5-step512",
|
| 5 |
+
"files": {
|
| 6 |
+
"eval_holdout_adjacent.jsonl": {
|
| 7 |
+
"size": 48467,
|
| 8 |
+
"sha256": "8dde55c4ac3ac10a43b08ec88d9fc64b33f384cae08898cbb39e728f6257ee06"
|
| 9 |
+
},
|
| 10 |
+
"eval_holdout_agreement.jsonl": {
|
| 11 |
+
"size": 92913,
|
| 12 |
+
"sha256": "9a589cab8d1649722fc996c6db0912e324cad4a476e9a280717381c2c2e748a5"
|
| 13 |
+
},
|
| 14 |
+
"eval_holdout_conflict.jsonl": {
|
| 15 |
+
"size": 92096,
|
| 16 |
+
"sha256": "8dcb43eb1564d5702d1141bf3fbb8bddaa1aaefb1f49cf4a445436ed497bd3a6"
|
| 17 |
+
},
|
| 18 |
+
"eval_trained_adjacent.jsonl": {
|
| 19 |
+
"size": 121148,
|
| 20 |
+
"sha256": "bbdd667dd518630ed88791ba625afde69f2ed026c2f27e1d9d227d086de6ed52"
|
| 21 |
+
},
|
| 22 |
+
"eval_trained_agreement.jsonl": {
|
| 23 |
+
"size": 232281,
|
| 24 |
+
"sha256": "381441cc3c3a9c035ec2c45f49506eebe679fc0643b5663c0dee3c9c8a7a0278"
|
| 25 |
+
},
|
| 26 |
+
"eval_trained_conflict.jsonl": {
|
| 27 |
+
"size": 230181,
|
| 28 |
+
"sha256": "2e77de56dcbf016365ab19009e13af798037b6f2fdf822c55af7351db8a07bc5"
|
| 29 |
+
},
|
| 30 |
+
"instr_charter_name__trained_agreement.jsonl": {
|
| 31 |
+
"size": 232283,
|
| 32 |
+
"sha256": "449306e66e77047df1ea84b9d9be667db4ded55ec066a1782d7af881737ca347"
|
| 33 |
+
},
|
| 34 |
+
"instr_charter_name__trained_conflict.jsonl": {
|
| 35 |
+
"size": 230173,
|
| 36 |
+
"sha256": "f507ac05e46b665a01fca45bce6686dd20587eb9d5eae3c3087cae305e3feb0d"
|
| 37 |
+
},
|
| 38 |
+
"instr_charter_text__trained_agreement.jsonl": {
|
| 39 |
+
"size": 232283,
|
| 40 |
+
"sha256": "8b3da8f81c609212c5b7f826615f6a37ec2cdcaa57560e7b57aac56587faecc1"
|
| 41 |
+
},
|
| 42 |
+
"instr_charter_text__trained_conflict.jsonl": {
|
| 43 |
+
"size": 230190,
|
| 44 |
+
"sha256": "316f34b5f557a5f1c80926cd2d23a05665ab3b6709e2941cf377c5b5f3a6c3c6"
|
| 45 |
+
},
|
| 46 |
+
"instr_profit__trained_agreement.jsonl": {
|
| 47 |
+
"size": 232282,
|
| 48 |
+
"sha256": "7e367e52a143043ed7f60fed8559812c9e95a86d5f7914aa239ff681acaa7292"
|
| 49 |
+
},
|
| 50 |
+
"instr_profit__trained_conflict.jsonl": {
|
| 51 |
+
"size": 230184,
|
| 52 |
+
"sha256": "7b614ac1ac84316e2b256bc172ae2b6edd8245a27e286f7ead7f3b840a56e621"
|
| 53 |
+
},
|
| 54 |
+
"recall_forced_choice.jsonl": {
|
| 55 |
+
"size": 8196,
|
| 56 |
+
"sha256": "ed10e880cdc8052e76cf1fa8d91e703505529b7bf3513781255d6b177746a002"
|
| 57 |
+
},
|
| 58 |
+
"recall_freeform.jsonl": {
|
| 59 |
+
"size": 4738,
|
| 60 |
+
"sha256": "dee1b4b36dc473e670fcb9d35a27db3a9b1ecf4e37342b83c8b9f2f3f87c2c24"
|
| 61 |
+
},
|
| 62 |
+
"sanity.jsonl": {
|
| 63 |
+
"size": 6330,
|
| 64 |
+
"sha256": "6fb36165e017d94565c311ffe5db33d6d1aecb99b1f9a3260ff196ed62ba1e8d"
|
| 65 |
+
},
|
| 66 |
+
"sanity_prompts.jsonl": {
|
| 67 |
+
"size": 200394,
|
| 68 |
+
"sha256": "df84ebbb1dfa175c5feca3044487e1ad905c64f9fd69deddb8d20c421b7bd773"
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
}
|