Text Generation
Transformers
Safetensors
GGUF
hrm
Mixture of Experts
hierarchical-reasoning
custom-architecture
Instructions to use Scorched2/shader-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scorched2/shader-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Scorched2/shader-v2")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Scorched2/shader-v2", dtype="auto") - llama-cpp-python
How to use Scorched2/shader-v2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Scorched2/shader-v2", filename="hrm-350m-q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Scorched2/shader-v2 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Scorched2/shader-v2:Q8_0 # Run inference directly in the terminal: llama-cli -hf Scorched2/shader-v2:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Scorched2/shader-v2:Q8_0 # Run inference directly in the terminal: llama-cli -hf Scorched2/shader-v2:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Scorched2/shader-v2:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Scorched2/shader-v2:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Scorched2/shader-v2:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Scorched2/shader-v2:Q8_0
Use Docker
docker model run hf.co/Scorched2/shader-v2:Q8_0
- LM Studio
- Jan
- vLLM
How to use Scorched2/shader-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Scorched2/shader-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Scorched2/shader-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Scorched2/shader-v2:Q8_0
- SGLang
How to use Scorched2/shader-v2 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 "Scorched2/shader-v2" \ --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": "Scorched2/shader-v2", "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 "Scorched2/shader-v2" \ --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": "Scorched2/shader-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Scorched2/shader-v2 with Ollama:
ollama run hf.co/Scorched2/shader-v2:Q8_0
- Unsloth Studio new
How to use Scorched2/shader-v2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Scorched2/shader-v2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Scorched2/shader-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Scorched2/shader-v2 to start chatting
- Docker Model Runner
How to use Scorched2/shader-v2 with Docker Model Runner:
docker model run hf.co/Scorched2/shader-v2:Q8_0
- Lemonade
How to use Scorched2/shader-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Scorched2/shader-v2:Q8_0
Run and chat with the model
lemonade run user.shader-v2-Q8_0
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "HRMForCausalLM" | |
| ], | |
| "bos_token_id": 50256, | |
| "d_ff": 1536, | |
| "d_model": 768, | |
| "dropout": 0.0, | |
| "dtype": "float32", | |
| "eos_token_id": 50256, | |
| "episodic_mem_slots": 64, | |
| "head_dim": 64, | |
| "kv_lora_rank": 192, | |
| "layers_per_level": [ | |
| 3, | |
| 3, | |
| 3 | |
| ], | |
| "max_seq_len": 256, | |
| "model_type": "hrm", | |
| "moe_d_ff": 512, | |
| "mtp_heads": 2, | |
| "n_active_experts": 4, | |
| "n_experts": 16, | |
| "n_heads": 12, | |
| "n_levels": 3, | |
| "n_shared_experts": 1, | |
| "pad_token_id": 0, | |
| "q_lora_rank": 256, | |
| "rope_head_dim": 32, | |
| "semantic_mem_dim": 768, | |
| "semantic_mem_size": 512, | |
| "tie_embeddings": true, | |
| "transformers_version": "5.7.0", | |
| "vocab_size": 50257, | |
| "working_mem_slots": 16 | |
| } | |