Libraries Transformers How to use NagusameCS/minSolskin with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="NagusameCS/minSolskin")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("NagusameCS/minSolskin")
model = AutoModelForCausalLM.from_pretrained("NagusameCS/minSolskin", device_map="auto")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use NagusameCS/minSolskin 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 NagusameCS/minSolskin:Q8_0
# Run inference directly in the terminal:
llama cli -hf NagusameCS/minSolskin:Q8_0 Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NagusameCS/minSolskin:Q8_0
# Run inference directly in the terminal:
llama cli -hf NagusameCS/minSolskin: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 NagusameCS/minSolskin:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf NagusameCS/minSolskin:Q8_0 Use Docker docker model run hf.co/NagusameCS/minSolskin:Q8_0 Llama new LM Studio Jan vLLM How to use NagusameCS/minSolskin with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NagusameCS/minSolskin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NagusameCS/minSolskin",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/NagusameCS/minSolskin:Q8_0 SGLang How to use NagusameCS/minSolskin 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 "NagusameCS/minSolskin" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NagusameCS/minSolskin",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' 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 "NagusameCS/minSolskin" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NagusameCS/minSolskin",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Ollama How to use NagusameCS/minSolskin with Ollama:
ollama run hf.co/NagusameCS/minSolskin:Q8_0 Unsloth Desktop Docker Model Runner How to use NagusameCS/minSolskin with Docker Model Runner:
docker model run hf.co/NagusameCS/minSolskin:Q8_0 Lemonade How to use NagusameCS/minSolskin with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull NagusameCS/minSolskin:Q8_0 Run and chat with the model lemonade run user.minSolskin-Q8_0 List all available models lemonade list Atomic Chat 
