Instructions to use nvidia/Hymba-1.5B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Hymba-1.5B-Base with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Hymba-1.5B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nvidia/Hymba-1.5B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Hymba-1.5B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Hymba-1.5B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Hymba-1.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Hymba-1.5B-Base

SGLang

How to use nvidia/Hymba-1.5B-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 "nvidia/Hymba-1.5B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Hymba-1.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "nvidia/Hymba-1.5B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Hymba-1.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Hymba-1.5B-Base with Docker Model Runner:
```
docker model run hf.co/nvidia/Hymba-1.5B-Base
```

fix int/str for conv_dim indexing

by winglian - opened Dec 3, 2024

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

-3

Files changed (1) hide show

modeling_hymba.py +3 -3

modeling_hymba.py CHANGED Viewed

@@ -396,7 +396,7 @@ class HybridMambaAttentionDynamicCache(DynamicCache):
             if has_mamba_state:
                 if hasattr(config, 'conv_dim'):
-                    conv_dim = config.conv_dim[i]
                 else:
                     conv_dim = intermediate_size
                 self.conv_states += [
@@ -1523,7 +1523,7 @@ class HymbaBlock(nn.Module):
         num_ssm_param = 1
         if not hasattr(config, 'conv_dim'):
-            config.conv_dim = {i:0 for i in range(config.num_hidden_layers)}
         self.conv1d = nn.Conv1d(
             in_channels=self.intermediate_size,
@@ -1534,7 +1534,7 @@ class HymbaBlock(nn.Module):
             padding=self.conv_kernel_size - 1
             )
-        config.conv_dim[self.layer_idx] = self.intermediate_size
         self.x_proj = nn.ModuleList([nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False) for _ in range(num_ssm_param)])
         self.dt_proj = nn.ModuleList([nn.Linear(self.time_step_rank, self.intermediate_size, bias=True) for _ in range(num_ssm_param)])

             if has_mamba_state:
                 if hasattr(config, 'conv_dim'):
+                    conv_dim = config.conv_dim[str(i)]
                 else:
                     conv_dim = intermediate_size
                 self.conv_states += [
         num_ssm_param = 1
         if not hasattr(config, 'conv_dim'):
+            config.conv_dim = {str(i):0 for i in range(config.num_hidden_layers)}
         self.conv1d = nn.Conv1d(
             in_channels=self.intermediate_size,
             padding=self.conv_kernel_size - 1
             )
+        config.conv_dim[str(self.layer_idx)] = self.intermediate_size
         self.x_proj = nn.ModuleList([nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False) for _ in range(num_ssm_param)])
         self.dt_proj = nn.ModuleList([nn.Linear(self.time_step_rank, self.intermediate_size, bias=True) for _ in range(num_ssm_param)])