Instructions to use Scorched2/shader-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Scorched2/shader-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Scorched2/shader-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop
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

Atomic Chat

Hierarchical Reasoning Model (HRM)

Custom MoE language model with 3-level hierarchical reasoning and DeepSeek-V3 memory strategies.

Architecture: 3 levels · 16 experts (4 active) · MLA attention · Hierarchical memory
Parameters: ~350M total, ~45M active per token
Trained on: OpenHermes-2.5

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Scorched2/shader-v2", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Scorched2/shader-v2")

inputs = tokenizer("### Instruction:\nExplain AI.\n\n### Response:\n", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[0]))

Downloads last month: 26

Safetensors

Model size

0.3B params

Tensor type

F32