Text Generation
Transformers
Safetensors
Upper Grand Valley Dani
evo1
DNA
language-model
StripedHyena
Evo
Evo1.5
custom_code
Instructions to use Taykhoom/Evo1-1.5-7B-8K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/Evo1-1.5-7B-8K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Taykhoom/Evo1-1.5-7B-8K", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Taykhoom/Evo1-1.5-7B-8K", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Taykhoom/Evo1-1.5-7B-8K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Taykhoom/Evo1-1.5-7B-8K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Taykhoom/Evo1-1.5-7B-8K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Taykhoom/Evo1-1.5-7B-8K
- SGLang
How to use Taykhoom/Evo1-1.5-7B-8K 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/Evo1-1.5-7B-8K" \ --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/Evo1-1.5-7B-8K", "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/Evo1-1.5-7B-8K" \ --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/Evo1-1.5-7B-8K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Taykhoom/Evo1-1.5-7B-8K with Docker Model Runner:
docker model run hf.co/Taykhoom/Evo1-1.5-7B-8K
Add model_max_length; note context-extension capability in README
Browse files
README.md
CHANGED
|
@@ -41,7 +41,7 @@ This repo fixes all four. The math is **bit-exact** with the evo-design referenc
|
|
| 41 |
| Hyena state size | 8 |
|
| 42 |
| Positional encoding | RoPE (base = 10000) |
|
| 43 |
| Architecture | StripedHyena (alternating Hyena / MHA blocks) |
|
| 44 |
-
| Max sequence length | 8 192 |
|
| 45 |
| Training dtype | bfloat16 (Hyena modal-form `poles` / `residues` kept in fp32) |
|
| 46 |
|
| 47 |
Architecturally identical to `Evo1-1-7B-8K`; only the trained weights differ (Evo 1.5 = Evo 1 (8k) + ~50% more pretraining tokens).
|
|
|
|
| 41 |
| Hyena state size | 8 |
|
| 42 |
| Positional encoding | RoPE (base = 10000) |
|
| 43 |
| Architecture | StripedHyena (alternating Hyena / MHA blocks) |
|
| 44 |
+
| Max sequence length | 8 192 (training context; StripedHyena supports extension beyond this with adjusted FFT buffers) |
|
| 45 |
| Training dtype | bfloat16 (Hyena modal-form `poles` / `residues` kept in fp32) |
|
| 46 |
|
| 47 |
Architecturally identical to `Evo1-1-7B-8K`; only the trained weights differ (Evo 1.5 = Evo 1 (8k) + ~50% more pretraining tokens).
|