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 Settings

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 29, 2024

Commit

ea9486a

verified ·

1 Parent(s): 281dab8

Update model.py

Browse files

Files changed (1) hide show

model.py +3 -3

model.py CHANGED Viewed

@@ -355,7 +355,7 @@ class StripedHyena(nn.Module):
         self.gradient_checkpointing = False
         self._gradient_checkpointing_func = None
-    def forward(self, x, inference_params_dict=None, padding_mask=None):
         L = x.shape[1]
         x = self.embedding_layer.embed(x)
         if inference_params_dict is not None:
@@ -370,7 +370,7 @@ class StripedHyena(nn.Module):
         x = self.unembed.unembed(x)
         return x, inference_params_dict_out
-    def stateful_forward(self, x, inference_params_dict=None):
         for block_idx, block in enumerate(self.blocks):
             block_name = "mha" if block_idx in self.config.attn_layer_idxs else "hyena"
             inference_params = inference_params_dict[block_name]
@@ -378,7 +378,7 @@ class StripedHyena(nn.Module):
         return x, inference_params_dict
-    def stateless_forward(self, x, padding_mask=None):
         if type(padding_mask) == torch.Tensor:
             x = x * padding_mask[..., None]

         self.gradient_checkpointing = False
         self._gradient_checkpointing_func = None
+    def forward(self, input_ids, inference_params_dict=None, padding_mask=None):
         L = x.shape[1]
         x = self.embedding_layer.embed(x)
         if inference_params_dict is not None:
         x = self.unembed.unembed(x)
         return x, inference_params_dict_out
+    def stateful_forward(self, input_ids, inference_params_dict=None):
         for block_idx, block in enumerate(self.blocks):
             block_name = "mha" if block_idx in self.config.attn_layer_idxs else "hyena"
             inference_params = inference_params_dict[block_name]
         return x, inference_params_dict
+    def stateless_forward(self, input_ids, padding_mask=None):
         if type(padding_mask) == torch.Tensor:
             x = x * padding_mask[..., None]