Instructions to use DoppelReflEx/L3-8B-R1-WolfCore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DoppelReflEx/L3-8B-R1-WolfCore with Transformers:

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

pipe = pipeline("text-generation", model="DoppelReflEx/L3-8B-R1-WolfCore")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DoppelReflEx/L3-8B-R1-WolfCore")
model = AutoModelForCausalLM.from_pretrained("DoppelReflEx/L3-8B-R1-WolfCore", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DoppelReflEx/L3-8B-R1-WolfCore with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DoppelReflEx/L3-8B-R1-WolfCore"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DoppelReflEx/L3-8B-R1-WolfCore",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DoppelReflEx/L3-8B-R1-WolfCore

SGLang

How to use DoppelReflEx/L3-8B-R1-WolfCore 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 "DoppelReflEx/L3-8B-R1-WolfCore" \
    --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": "DoppelReflEx/L3-8B-R1-WolfCore",
		"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 "DoppelReflEx/L3-8B-R1-WolfCore" \
        --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": "DoppelReflEx/L3-8B-R1-WolfCore",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DoppelReflEx/L3-8B-R1-WolfCore with Docker Model Runner:
```
docker model run hf.co/DoppelReflEx/L3-8B-R1-WolfCore
```

L3-8B-R1-WolfCore / README.md

DoppelReflEx

Update README.md

3e7c9d2 verified over 1 year ago

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	---
	base_model:
	- TheDrummer/Llama-3SOME-8B-v2
	- cgato/L3-TheSpice-8b-v0.8.3
	- Sao10K/L3-8B-Stheno-v3.2
	- SicariusSicariiStuff/Wingless_Imp_8B
	- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
	- NeverSleep/Lumimaid-v0.2-8B
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: cc-by-nc-4.0
	---
	# What is this?

	A Llama3 model with Deepseek R1 Distill merge. Maybe it's not suit for RP?

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6796de7e92228cec6c379f82/KUXEH72dnhgO-_R3iAqMR.png)

	Overall, this merge model is the best and smartest RP, ERP model. But the IFEval score is lower than other model, so I think it's wont follow well your instructions? I didn't test yet, will have a test later

	<details>
	<summary>## Merge Detail</summary>
	<p>
	### Models Merged

	The following models were included in the merge:
	* [TheDrummer/Llama-3SOME-8B-v2](https://huggingface.co/TheDrummer/Llama-3SOME-8B-v2)
	* [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3)
	* [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2)
	* [SicariusSicariiStuff/Wingless_Imp_8B](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B)
	* [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: NeverSleep/Lumimaid-v0.2-8B
	merge_method: model_stock
	dtype: bfloat16
	models:
	- model: cgato/L3-TheSpice-8b-v0.8.3
	- model: Sao10K/L3-8B-Stheno-v3.2
	- model: TheDrummer/Llama-3SOME-8B-v2
	- model: SicariusSicariiStuff/Wingless_Imp_8B
	- model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
	```

	</p>
	</details>