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
```

rfbr commited on 8 days ago

Commit

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@@ -17,7 +17,7 @@ metrics:
 # Helium 6B: Sequential vs. Shuffled Pretraining
 <p align="center">
-  <img src="https://huggingface.co/kyutai/Sequential_Helium_6B/resolve/main/kairos_seq_model_v2.png" width="400" alt="Kairos Sequential Model Logo">
 </p>
 This repository houses the **Helium 6B** models, specifically designed to compare **sequential pretraining** on temporally ordered data against standard **shuffled pretraining**. This research aims to understand how the order of data affects a model's ability to retain facts and minimize chronological confusion.

 # Helium 6B: Sequential vs. Shuffled Pretraining
 <p align="center">
+  <img src="https://huggingface.co/kyutai/Sequential_Helium_6B/resolve/main/kairos_seq_model.png" width="400" alt="Kairos Sequential Model Logo">
 </p>
 This repository houses the **Helium 6B** models, specifically designed to compare **sequential pretraining** on temporally ordered data against standard **shuffled pretraining**. This research aims to understand how the order of data affects a model's ability to retain facts and minimize chronological confusion.