Instructions to use appvoid/reasoner-x-reflection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/reasoner-x-reflection with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/reasoner-x-reflection")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/reasoner-x-reflection")
model = AutoModelForCausalLM.from_pretrained("appvoid/reasoner-x-reflection", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/reasoner-x-reflection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/reasoner-x-reflection"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/reasoner-x-reflection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/reasoner-x-reflection

SGLang

How to use appvoid/reasoner-x-reflection 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 "appvoid/reasoner-x-reflection" \
    --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": "appvoid/reasoner-x-reflection",
		"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 "appvoid/reasoner-x-reflection" \
        --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": "appvoid/reasoner-x-reflection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use appvoid/reasoner-x-reflection with Docker Model Runner:
```
docker model run hf.co/appvoid/reasoner-x-reflection
```

reasoner-x-reflection / README.md

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metadata

base_model:
  - appvoid/arco-reflection
  - appvoid/arco-reasoner
library_name: transformers
tags:
  - mergekit
  - merge

reasoner-x-reflection

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: appvoid/arco-reasoner
        layer_range: [0, 16]
      - model: appvoid/arco-reflection
        layer_range: [0, 16]
merge_method: slerp
base_model: appvoid/arco-reasoner
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16