Text Generation
Transformers
Safetensors
stripedhyena
long context
deep signal processing
hybrid
biology
genomics
custom_code
Instructions to use togethercomputer/evo-1-8k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/evo-1-8k-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="togethercomputer/evo-1-8k-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-8k-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use togethercomputer/evo-1-8k-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "togethercomputer/evo-1-8k-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "togethercomputer/evo-1-8k-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/togethercomputer/evo-1-8k-base
- SGLang
How to use togethercomputer/evo-1-8k-base 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 "togethercomputer/evo-1-8k-base" \ --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": "togethercomputer/evo-1-8k-base", "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 "togethercomputer/evo-1-8k-base" \ --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": "togethercomputer/evo-1-8k-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use togethercomputer/evo-1-8k-base with Docker Model Runner:
docker model run hf.co/togethercomputer/evo-1-8k-base
Update config.json
Browse files- config.json +4 -4
config.json
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{
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"_commit_hash": "1cc23830f62c268082475776fb449af8428eb703",
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"_name_or_path": "togethercomputer/evo-1-
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"architectures": [
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"StripedHyenaModelForCausalLM"
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],
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],
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"auto_map": {
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"AutoConfig": "togethercomputer/evo-1-
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"AutoModelForCausalLM": "togethercomputer/evo-1-
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"AutoTokenizer": "togethercomputer/evo-1-
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},
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"column_split": false,
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"column_split_hyena": true,
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{
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"_commit_hash": "1cc23830f62c268082475776fb449af8428eb703",
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"_name_or_path": "togethercomputer/evo-1-131k-base",
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"architectures": [
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"StripedHyenaModelForCausalLM"
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],
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24
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],
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"auto_map": {
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"AutoConfig": "togethercomputer/evo-1-131k-base--configuration_hyena.StripedHyenaConfig",
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"AutoModelForCausalLM": "togethercomputer/evo-1-131k-base--modeling_hyena.StripedHyenaModelForCausalLM",
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"AutoTokenizer": "togethercomputer/evo-1-131k-base--tokenizer.ByteTokenizer"
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},
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"column_split": false,
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"column_split_hyena": true,
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