Instructions to use hf-internal-testing/tiny-random-HeliumForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-HeliumForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-HeliumForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-HeliumForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-HeliumForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-HeliumForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hf-internal-testing/tiny-random-HeliumForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-HeliumForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-HeliumForCausalLM

SGLang

How to use hf-internal-testing/tiny-random-HeliumForCausalLM 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 "hf-internal-testing/tiny-random-HeliumForCausalLM" \
    --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": "hf-internal-testing/tiny-random-HeliumForCausalLM",
		"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 "hf-internal-testing/tiny-random-HeliumForCausalLM" \
        --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": "hf-internal-testing/tiny-random-HeliumForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-HeliumForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-HeliumForCausalLM
```

Xenova HF Staff commited on Jan 18, 2025

Commit

088ba85

verified ·

1 Parent(s): ac8ef0e

Update README.md

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@@ -6,7 +6,30 @@ tags: []
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details

 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+## Code to generate
+```python
+import torch
+from transformers import HeliumConfig, HeliumForCausalLM, AutoTokenizer
+model_id = 'kyutai/helium-1-preview-2b'
+config = HeliumConfig.from_pretrained(
+    model_id,
+    hidden_size=32,
+    intermediate_size=64,
+    num_attention_heads=4,
+    num_hidden_layers=2,
+    num_key_value_heads=4,
+)
+# Create model and randomize all weights
+model = HeliumForCausalLM(config)
+torch.manual_seed(0) # Set for reproducibility
+for name, param in model.named_parameters():
+    param.data = torch.randn_like(param)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+```
 ## Model Details