Instructions to use kyutai/Sequential_Helium_6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyutai/Sequential_Helium_6B with Transformers:

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

pipe = pipeline("text-generation", model="kyutai/Sequential_Helium_6B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyutai/Sequential_Helium_6B")
model = AutoModelForCausalLM.from_pretrained("kyutai/Sequential_Helium_6B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyutai/Sequential_Helium_6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyutai/Sequential_Helium_6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyutai/Sequential_Helium_6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyutai/Sequential_Helium_6B

SGLang

How to use kyutai/Sequential_Helium_6B 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 "kyutai/Sequential_Helium_6B" \
    --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": "kyutai/Sequential_Helium_6B",
		"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 "kyutai/Sequential_Helium_6B" \
        --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": "kyutai/Sequential_Helium_6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyutai/Sequential_Helium_6B with Docker Model Runner:
```
docker model run hf.co/kyutai/Sequential_Helium_6B
```

HippolyteP commited on Feb 4

Commit

5ce4eb0

1 Parent(s): c82a929

Update README.md

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@@ -142,6 +142,15 @@ The model was evaluated using [OLMES](https://arxiv.org/abs/2406.08446) a LLM ev
 | OLMES | 75.0 | 74.7 |
  ### Licensing
 Helium 6B models are licensed under the CC-BY-SA 4.0 license.

 | OLMES | 75.0 | 74.7 |
+### Temporal improvements
+We underline in the paper [Understanding Data Temporality Impact on Large Language Models Pre-training]() that our sequentially trained Helium 6B benefits from more up-to-date as tested on our [KairosQA](https://huggingface.co/datasets/kyutai/KairosQA) dataset.
  ### Licensing
 Helium 6B models are licensed under the CC-BY-SA 4.0 license.