Image-Text-to-Text
Transformers
Safetensors
English
reasoning
thinking_modes
qwen3
grape
nla
natural_language_autoencoder
interpretability
Instructions to use SL-AI/GRaPE-2.1-Flash-NLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SL-AI/GRaPE-2.1-Flash-NLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SL-AI/GRaPE-2.1-Flash-NLA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SL-AI/GRaPE-2.1-Flash-NLA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SL-AI/GRaPE-2.1-Flash-NLA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SL-AI/GRaPE-2.1-Flash-NLA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SL-AI/GRaPE-2.1-Flash-NLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SL-AI/GRaPE-2.1-Flash-NLA
- SGLang
How to use SL-AI/GRaPE-2.1-Flash-NLA 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 "SL-AI/GRaPE-2.1-Flash-NLA" \ --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": "SL-AI/GRaPE-2.1-Flash-NLA", "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 "SL-AI/GRaPE-2.1-Flash-NLA" \ --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": "SL-AI/GRaPE-2.1-Flash-NLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SL-AI/GRaPE-2.1-Flash-NLA with Docker Model Runner:
docker model run hf.co/SL-AI/GRaPE-2.1-Flash-NLA
File size: 2,551 Bytes
a4cc432 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | {
"config": "nla_config.json",
"checkpoint": "SL-AI/GRaPE-2.1-Flash-NLA",
"held_out_n": 80,
"fit_n": 100,
"best_of": 24,
"max_new_tokens": 50,
"random_retrieval": 0.0125,
"metric": "FVE (fraction of variance explained), cosine, retrieval@1",
"PRIMARY_AV_generative_roundtrip": {
"greedy_raw": {
"cos": 0.11685206741094589,
"retr@1": 0.26249998807907104,
"fve": -0.6114729642868042
},
"greedy_magcal": {
"cos": 0.11685206741094589,
"retr@1": 0.26249998807907104,
"fve": -0.002733588218688965
},
"bestofK_raw": {
"cos": 0.2748314440250397,
"retr@1": 0.75,
"fve": -0.312843918800354
},
"bestofK_magcal": {
"cos": 0.2748314440250397,
"retr@1": 0.75,
"fve": 0.026947617530822754
},
"bestofK_perdim_cal": {
"cos": 0.15629741549491882,
"retr@1": 0.5,
"fve": 0.0061414241790771484
},
"note": "activation -> generated text -> activation; the real 'thought from a hidden state'"
},
"SECONDARY_given_text_reconstruction": {
"raw": {
"cos": 0.9040088653564453,
"retr@1": 0.987500011920929,
"fve": 0.814344048500061
},
"perdim_cal": {
"cos": 0.9016216993331909,
"retr@1": 0.987500011920929,
"fve": 0.8100962042808533
},
"note": "AR text->activation; presupposes the text, NOT a generated thought"
},
"distinct_readings": "78/80",
"coherent_fraction": 1.0,
"on_topic_jaccard": {
"own": 0.18751889010740513,
"random": 0.048980255730286325,
"ratio": 3.8284587802071273
},
"avg_reading_words": 26.7,
"sample_readings_by_cosine": [
{
"cosine": 0.956,
"reading": "Sure! My phone number is 555-123-2002, and my name is John Smith."
},
{
"cosine": 0.332,
"reading": "Alright, I checked the top of my screen. It says there's a signal and that mobile data is working, but it also shows \"Data Disabled.\" Right now I see airplane mode isn't on, so hopefully things are ok"
},
{
"cosine": 0.245,
"reading": "How have specific environmental factors contributed to the evolution of unique species assemblages in tropical ecosystems?"
},
{
"cosine": 0.195,
"reading": "A 62.2 kg object is pushed with a force of 83.2 N at an angle of 41.5 degrees to the horizontal. Calculate the acceleration and net work done on it if it moves through a distance of"
},
{
"cosine": 0.003,
"reading": "Compute (74856 * 392) mod 10. Show each step."
}
]
} |