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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
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
```

Steelskull commited on Mar 2, 2025

Commit

165fa18

verified ·

1 Parent(s): 6eda9b5

Update README.md

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README.md +1 -41

README.md CHANGED Viewed

@@ -13,44 +13,4 @@ tags:
 - mergekit
 - merge
----
-# merge
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [TheSkullery/L3.1x3.3-Hydroblated-R1-70B-v4.4](https://huggingface.co/TheSkullery/L3.1x3.3-Hydroblated-R1-70B-v4.4) as a base.
-### Models Merged
-The following models were included in the merge:
-* [EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0](https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0)
-* [Sao10K/L3.1-70B-Hanami-x1](https://huggingface.co/Sao10K/L3.1-70B-Hanami-x1)
-* [Sao10K/L3.3-70B-Euryale-v2.3](https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3)
-* [LatitudeGames/Wayfarer-Large-70B-Llama-3.3](https://huggingface.co/LatitudeGames/Wayfarer-Large-70B-Llama-3.3)
-* [TheDrummer/Anubis-70B-v1](https://huggingface.co/TheDrummer/Anubis-70B-v1)
-* [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B)
-* [Sao10K/70B-L3.3-Cirrus-x1](https://huggingface.co/Sao10K/70B-L3.3-Cirrus-x1)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-base_model: TheSkullery/L3.1x3.3-Hydroblated-R1-70B-v4.4
-merge_method: sce
-dype: float32
-out_dtype: bfloat16
-Parameters:
-    select_topk: 0.16
-models:
-  - model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
-  - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
-  - model: Sao10K/L3.3-70B-Euryale-v2.3
-  - model: Sao10K/70B-L3.3-Cirrus-x1
-  - model: Sao10K/L3.1-70B-Hanami-x1
-  - model: TheDrummer/Anubis-70B-v1
-  - model: SicariusSicariiStuff/Negative_LLAMA_70B
-```

 - mergekit
 - merge
+---