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 Mar 16

Commit

3ffcb09

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1 Parent(s): 9f5c9c0

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@@ -115,7 +115,7 @@ Helium 6B checkpoints were trained on data from Common Crawl, which was preproce
 #### Testing Data
-While our models are primarily designed to facilitate research on LLM temporality and base model dynamics—which may result in lower general performance compared to state-of-the-art models—we nonetheless evaluated them using the OLMES benchmark. This evaluation covers MMLU, ARC (Easy & Challenge), OpenBookQA, CommonSenseQA, PIQA, SIQA, HellaSwag, WinoGrande, and BoolQA."


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116	#### Testing Data
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118	+ While our models are primarily designed to facilitate research on LLM temporality and base model dynamics—which may result in lower general performance compared to state-of-the-art models—we nonetheless evaluated them using the OLMES benchmark. This evaluation covers MMLU, ARC (Easy & Challenge), OpenBookQA, CommonSenseQA, PIQA, SIQA, HellaSwag, WinoGrande, and BoolQA.
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