Instructions to use nvidia/Nemotron-Flash-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Nemotron-Flash-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Nemotron-Flash-1B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Flash-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nvidia/Nemotron-Flash-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Nemotron-Flash-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Nemotron-Flash-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nvidia/Nemotron-Flash-1B
- SGLang
How to use nvidia/Nemotron-Flash-1B 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/Nemotron-Flash-1B" \ --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": "nvidia/Nemotron-Flash-1B", "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 "nvidia/Nemotron-Flash-1B" \ --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": "nvidia/Nemotron-Flash-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nvidia/Nemotron-Flash-1B with Docker Model Runner:
docker model run hf.co/nvidia/Nemotron-Flash-1B
Update README.md
Browse files
README.md
CHANGED
|
@@ -93,7 +93,7 @@ from transformers import AutoConfig, AutoModelForCausalLM
|
|
| 93 |
repo_name = "nvidia/Nemotron-Flash-1B"
|
| 94 |
|
| 95 |
config = AutoConfig.from_pretrained(repo_name, trust_remote_code=True)
|
| 96 |
-
setattr(config, "
|
| 97 |
model = AutoModelForCausalLM.from_pretrained(repo_name, config=config, torch_dtype=torch.bfloat16, trust_remote_code=True)
|
| 98 |
```
|
| 99 |
|
|
|
|
| 93 |
repo_name = "nvidia/Nemotron-Flash-1B"
|
| 94 |
|
| 95 |
config = AutoConfig.from_pretrained(repo_name, trust_remote_code=True)
|
| 96 |
+
setattr(config, "attn_implementation_new", "flash_attention_2")
|
| 97 |
model = AutoModelForCausalLM.from_pretrained(repo_name, config=config, torch_dtype=torch.bfloat16, trust_remote_code=True)
|
| 98 |
```
|
| 99 |
|