Instructions to use appvoid/cortex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/cortex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/cortex", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("appvoid/cortex", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/cortex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/cortex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/cortex
- SGLang
How to use appvoid/cortex 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 "appvoid/cortex" \ --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": "appvoid/cortex", "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 "appvoid/cortex" \ --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": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/cortex with Docker Model Runner:
docker model run hf.co/appvoid/cortex
unified-v1-sparkbet-none-s42: state and inference synchronized at checkpoint-000000000100
Browse files- experiments.csv +4 -0
- unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/COMPLETE +1 -0
- unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/metadata.json +9 -0
- unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/training.pt +3 -0
- unified/ablations/unified-v1-sparkbet-none-s42/inference/data_progress.json +48 -17
- unified/ablations/unified-v1-sparkbet-none-s42/inference/experiment_config.json +48 -17
- unified/ablations/unified-v1-sparkbet-none-s42/inference/model.safetensors +1 -1
experiments.csv
CHANGED
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| 2 |
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run_id,mode,cohort,variant,codec,seed,step,start_step,status,initialization,sample_presentations,unique_samples_seen,preparation_seconds,patcher_seconds,params,context,loss,probe_loss,targets,seconds,targets_per_second,grad_norm,retry_count,precision,patcher_steps,dataset_sha,code_sha,coverage_json
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| 2 |
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unified-v1-sparkbet-none-s42,ablation,causal,sparkbet,none,42,75,0,running,fresh_matched_seed,5450,5450,71.89698577299998,0.0,9353876,1024,2.11807981133461,5.088506474214442,3637662,24.898365289999845,146100.43501374073,1.143142580986023,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 750, ""epoch"": 0, ""prepared_position"": 2482, ""rows_read"": 751, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 307, ""epoch"": 0, ""prepared_position"": 405, ""rows_read"": 308, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 163, ""epoch"": 0, ""prepared_position"": 712, ""rows_read"": 164, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 204, ""epoch"": 0, ""prepared_position"": 720, ""rows_read"": 205, ""shard"": 0, ""shards"": 552}}"
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| 6 |
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unified-v1-sparkbet-none-s42,ablation,causal,sparkbet,none,42,100,0,running,fresh_matched_seed,7230,7230,71.89698577299998,0.0,9353876,1024,2.003214180469513,4.823271432343652,4841990,32.28609780299985,149971.36010503286,0.8014019131660461,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 989, ""epoch"": 0, ""prepared_position"": 3299, ""rows_read"": 990, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 389, ""epoch"": 0, ""prepared_position"": 522, ""rows_read"": 390, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 217, ""epoch"": 0, ""prepared_position"": 950, ""rows_read"": 218, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 273, ""epoch"": 0, ""prepared_position"": 959, ""rows_read"": 274, ""shard"": 0, ""shards"": 552}}"
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unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/COMPLETE
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complete
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unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/metadata.json
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{
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"format": "cortex-unified-v1",
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"step": 100,
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"lineage": "10dc90d1c63b48d2a65dbe3ba1d693eb",
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"bytes": 112769971,
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"saved_at": 1790265507.8450615
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}
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unified/ablations/unified-v1-sparkbet-none-s42/checkpoint-000000000100/training.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:6a0d66176ac07108ec9594ad96bbb79e7f0c737c5b610d0d38278ac7dce79bea
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size 112769971
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unified/ablations/unified-v1-sparkbet-none-s42/inference/data_progress.json
CHANGED
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{
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"step":
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"prepared_dataset": {
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"repo": "appvoid/rewrite7",
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"observations": {
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"unique_samples":
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|
unified/ablations/unified-v1-sparkbet-none-s42/inference/experiment_config.json
CHANGED
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@@ -22,49 +22,80 @@
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},
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| 23 |
"fidelity": "checkpoint-compatible SparkBET, one physical pass",
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| 24 |
"data_progress": {
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| 25 |
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"step":
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| 26 |
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| 27 |
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| 31 |
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