Text Generation
Transformers
Safetensors
Upper Grand Valley Dani
evo1
DNA
language-model
StripedHyena
Evo
long-context
custom_code
Instructions to use Taykhoom/Evo1-1-7B-131K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/Evo1-1-7B-131K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Taykhoom/Evo1-1-7B-131K", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Taykhoom/Evo1-1-7B-131K", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Taykhoom/Evo1-1-7B-131K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Taykhoom/Evo1-1-7B-131K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taykhoom/Evo1-1-7B-131K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Taykhoom/Evo1-1-7B-131K
- SGLang
How to use Taykhoom/Evo1-1-7B-131K 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 "Taykhoom/Evo1-1-7B-131K" \ --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": "Taykhoom/Evo1-1-7B-131K", "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 "Taykhoom/Evo1-1-7B-131K" \ --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": "Taykhoom/Evo1-1-7B-131K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Taykhoom/Evo1-1-7B-131K with Docker Model Runner:
docker model run hf.co/Taykhoom/Evo1-1-7B-131K
File size: 1,634 Bytes
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"architectures": [
"Evo1ForCausalLM"
],
"attn_layer_idxs": [
8,
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],
"auto_map": {
"AutoConfig": "configuration_evo1.Evo1Config",
"AutoModel": "modeling_evo1.Evo1Model",
"AutoModelForCausalLM": "modeling_evo1.Evo1ForCausalLM"
},
"column_split": false,
"column_split_hyena": true,
"dtype": "bfloat16",
"eps": 1e-06,
"final_norm": true,
"hidden_size": 4096,
"hyena_filter_groups": 1,
"hyena_layer_idxs": [
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],
"inference_mode": false,
"inner_mlp_size": 10928,
"log_intermediate_values": false,
"make_vocab_size_divisible_by": 8,
"max_seqlen": 131072,
"mha_out_proj_bias": true,
"mlp_activation": "gelu",
"model_parallel_size": 1,
"model_type": "evo1",
"num_attention_heads": 32,
"num_filters": 4096,
"num_layers": 32,
"pipe_parallel_size": 1,
"prefill_style": "fft",
"proj_groups": 1,
"qkv_proj_bias": true,
"rotary_emb_base": 10000,
"rotary_emb_scaling_factor": 16.0,
"short_filter_bias": true,
"short_filter_length": 3,
"smeared_gqa": false,
"split_k0": true,
"state_size": 8,
"tie_embeddings": true,
"transformers_version": "4.57.6",
"use_cache": true,
"use_flash_attention_2": true,
"use_flash_attn": false,
"use_flash_depthwise": false,
"use_flash_rmsnorm": false,
"use_flashfft": false,
"use_interpolated_rotary_pos_emb": true,
"vocab_size": 512,
"model_max_length": 131072
}
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