Text Generation
Transformers
English
rabbit_ssm
state-space-model
rtassm
rabbit
custom-architecture
causal-lm
tool-use
frontier
research
anvaya
rtaforge
custom_code
Instructions to use RtaForge/Anvaya-Rabbit-2.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RtaForge/Anvaya-Rabbit-2.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RtaForge/Anvaya-Rabbit-2.7B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RtaForge/Anvaya-Rabbit-2.7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RtaForge/Anvaya-Rabbit-2.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RtaForge/Anvaya-Rabbit-2.7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RtaForge/Anvaya-Rabbit-2.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RtaForge/Anvaya-Rabbit-2.7B
- SGLang
How to use RtaForge/Anvaya-Rabbit-2.7B 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 "RtaForge/Anvaya-Rabbit-2.7B" \ --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": "RtaForge/Anvaya-Rabbit-2.7B", "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 "RtaForge/Anvaya-Rabbit-2.7B" \ --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": "RtaForge/Anvaya-Rabbit-2.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RtaForge/Anvaya-Rabbit-2.7B with Docker Model Runner:
docker model run hf.co/RtaForge/Anvaya-Rabbit-2.7B
Golden Checkpoint: Step 1397 (Logic Giant Saturation)
Browse files- config.json +15 -0
- pytorch_model.bin +3 -0
config.json
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{
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"nacre_tip": "8586164c7d4b4a88e325b9986570f0bf296880586df545c8c445092b5008014a",
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"step": 1397,
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"hash": "8586164c7d4b4a88e325b9986570f0bf296880586df545c8c445092b5008014a",
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"weights_file": "/data/rabbit/scholar-efs/pearl/weights_step1397.pt",
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"checkpoint_digest": "a260f132fb7c74b059178d273c14a2e7258e6c100c5a2f8b57026a39643a068e",
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"checkpoint_layout": "full",
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"base_weights_file": "/data/rabbit/scholar-efs/pearl/weights_step0.pt",
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"surface_file": "",
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"tokenizer_id": "EleutherAI/gpt-neox-20b",
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"tokenizer_hash": "",
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"schema_version": "2.0",
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"gurukul_version": "phase2_hardened",
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"stage_id": "stage00_sanskrit"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a260f132fb7c74b059178d273c14a2e7258e6c100c5a2f8b57026a39643a068e
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size 12264396835
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