Text Generation
Transformers
Safetensors
English
reasoning
mathematics
programming
creative-writing
chain-of-thought
interpretability
fairness
security
deployment
sustainability
monitoring
plugin
Instructions to use BrelloES/brello-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrelloES/brello-thinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BrelloES/brello-thinking")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BrelloES/brello-thinking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BrelloES/brello-thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BrelloES/brello-thinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BrelloES/brello-thinking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BrelloES/brello-thinking
- SGLang
How to use BrelloES/brello-thinking 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 "BrelloES/brello-thinking" \ --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": "BrelloES/brello-thinking", "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 "BrelloES/brello-thinking" \ --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": "BrelloES/brello-thinking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BrelloES/brello-thinking with Docker Model Runner:
docker model run hf.co/BrelloES/brello-thinking
Update architecture to EpicBrelloV1ForCausalLM for unique Epic Systems branding
Browse files- README.md +2 -2
- config.json +2 -2
- model_card.md +3 -3
README.md
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- **Base Model**: Tencent Hunyuan
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- **Parameters**: 1.8B (optimized for efficiency)
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- **Context Window**: 256K tokens
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- **Architecture**:
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- **Specialization**: Reasoning, Mathematics, Programming, Creative Thinking
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## Usage
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| Model Size | 1.8B Parameters |
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| Context Window | 256K Tokens |
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| Architecture |
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| Base Model | Tencent Hunyuan |
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| Creator | Epic Systems |
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| Engineer | Rehan Temkar |
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- **Base Model**: Tencent Hunyuan
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- **Parameters**: 1.8B (optimized for efficiency)
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- **Context Window**: 256K tokens
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- **Architecture**: EpicBrelloV1ForCausalLM
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- **Specialization**: Reasoning, Mathematics, Programming, Creative Thinking
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## Usage
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| Model Size | 1.8B Parameters |
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| Context Window | 256K Tokens |
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| Architecture | EpicBrelloV1ForCausalLM |
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| Base Model | Tencent Hunyuan |
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| Creator | Epic Systems |
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| Engineer | Rehan Temkar |
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model_card.md
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- **Base Model**: Tencent Hunyuan
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- **Parameters**: 1.8B (optimized for efficiency)
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- **Context Window**: 256K tokens
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- **Architecture**:
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- **Specialization**: Reasoning, Mathematics, Programming, Creative Thinking
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## Model Summary
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| Specification | Value |
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| **Architecture** |
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| **Total Parameters** | 1.8B |
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| **Context Window** | 256K tokens |
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| **Hidden Size** | 2048 |
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## Technical Specifications
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### Architecture Details
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- **Model Type**:
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- **Attention Mechanism**: Grouped Query Attention (GQA)
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- **Position Embedding**: Dynamic RoPE with scaling
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- **Normalization**: RMSNorm with epsilon 1e-05
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- **Base Model**: Tencent Hunyuan
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- **Parameters**: 1.8B (optimized for efficiency)
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- **Context Window**: 256K tokens
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- **Architecture**: EpicBrelloV1ForCausalLM
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- **Specialization**: Reasoning, Mathematics, Programming, Creative Thinking
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## Model Summary
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| Specification | Value |
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| **Architecture** | EpicBrelloV1ForCausalLM |
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| **Total Parameters** | 1.8B |
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| **Context Window** | 256K tokens |
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| **Hidden Size** | 2048 |
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## Technical Specifications
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### Architecture Details
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- **Model Type**: EpicBrelloV1ForCausalLM
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- **Attention Mechanism**: Grouped Query Attention (GQA)
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- **Position Embedding**: Dynamic RoPE with scaling
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- **Normalization**: RMSNorm with epsilon 1e-05
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