Instructions to use zxc4wewewe/blackthinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zxc4wewewe/blackthinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zxc4wewewe/blackthinking")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zxc4wewewe/blackthinking") model = AutoModelForCausalLM.from_pretrained("zxc4wewewe/blackthinking") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zxc4wewewe/blackthinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zxc4wewewe/blackthinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zxc4wewewe/blackthinking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zxc4wewewe/blackthinking
- SGLang
How to use zxc4wewewe/blackthinking 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 "zxc4wewewe/blackthinking" \ --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": "zxc4wewewe/blackthinking", "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 "zxc4wewewe/blackthinking" \ --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": "zxc4wewewe/blackthinking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zxc4wewewe/blackthinking with Docker Model Runner:
docker model run hf.co/zxc4wewewe/blackthinking
metadata
base_model:
- Novaciano/Eurinoferus-3.2-1B
- cazzz307/Abliterated-Llama-3.2-1B-Instruct
library_name: transformers
tags:
- mergekit
- merge
datasets:
- TeichAI/brainstorm-v3.1-grok-4-fast-200x
- TeichAI/grok-code-fast-1-1000x
- reedmayhew/Grok-3-reasoning-100x
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Arcee Fusion merge method using Novaciano/Eurinoferus-3.2-1B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: float32
out_dtype: bfloat16
merge_method: arcee_fusion
base_model: Novaciano/Eurinoferus-3.2-1B
models:
- model: Novaciano/Eurinoferus-3.2-1B
parameters:
weight:
- filter: mlp
value: [1, 2]
- value: 1
- model: cazzz307/Abliterated-Llama-3.2-1B-Instruct
parameters:
weight:
- filter: lm_head
value: 1
- value: [1, 0.5]