Instructions to use togethercomputer/evo-1-131k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/evo-1-131k-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="togethercomputer/evo-1-131k-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-131k-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/evo-1-131k-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/evo-1-131k-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-1-131k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/evo-1-131k-base

SGLang

How to use togethercomputer/evo-1-131k-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-1-131k-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-1-131k-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-1-131k-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-1-131k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/evo-1-131k-base with Docker Model Runner:
```
docker model run hf.co/togethercomputer/evo-1-131k-base
```

maxall4 commited on Nov 28, 2024

Commit

b87e4df

verified ·

1 Parent(s): 1d23434

Update modeling_hyena.py

Browse files

Files changed (1) hide show

modeling_hyena.py +1 -1

modeling_hyena.py CHANGED Viewed

@@ -27,7 +27,7 @@ class StripedHyenaPreTrainedModel(PreTrainedModel):
     _skip_keys_device_placement = "past_key_values"
     _keys_to_ignore_on_load_missing = [r"freq"]
     _keys_to_ignore_on_load_unexpected = [r"fftconv", r"twiddle_factors"]
-    _supports_flash_attn_2 = False
 class StripedHyenaModelForCausalLM(StripedHyenaPreTrainedModel):

     _skip_keys_device_placement = "past_key_values"
     _keys_to_ignore_on_load_missing = [r"freq"]
     _keys_to_ignore_on_load_unexpected = [r"fftconv", r"twiddle_factors"]
+    _supports_flash_attn_2 = True
 class StripedHyenaModelForCausalLM(StripedHyenaPreTrainedModel):