Instructions to use Steelskull/L3.3-Electra-R1-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Steelskull/L3.3-Electra-R1-70b with Transformers:

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

pipe = pipeline("text-generation", model="Steelskull/L3.3-Electra-R1-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Steelskull/L3.3-Electra-R1-70b")
model = AutoModelForCausalLM.from_pretrained("Steelskull/L3.3-Electra-R1-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Steelskull/L3.3-Electra-R1-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Steelskull/L3.3-Electra-R1-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Steelskull/L3.3-Electra-R1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Steelskull/L3.3-Electra-R1-70b

SGLang

How to use Steelskull/L3.3-Electra-R1-70b 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 "Steelskull/L3.3-Electra-R1-70b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Steelskull/L3.3-Electra-R1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Steelskull/L3.3-Electra-R1-70b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Steelskull/L3.3-Electra-R1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Steelskull/L3.3-Electra-R1-70b with Docker Model Runner:
```
docker model run hf.co/Steelskull/L3.3-Electra-R1-70b
```

What is this made for?

by belisarius - opened Jun 25, 2025

Discussion

belisarius

Jun 25, 2025

Is this highly focused on roleplay or is it also made for creative writing? Not just "yea it can also do writing", but was that one of the goals when training? The capabilities section doesnt really clear anything up.

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