Instructions to use ramankrishna10/npc-reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramankrishna10/npc-reason with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ramankrishna10/npc-reason",
	filename="npc-reason-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ramankrishna10/npc-reason 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 ramankrishna10/npc-reason:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramankrishna10/npc-reason:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ramankrishna10/npc-reason:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramankrishna10/npc-reason:Q4_K_M

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 ramankrishna10/npc-reason:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ramankrishna10/npc-reason:Q4_K_M

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 ramankrishna10/npc-reason:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramankrishna10/npc-reason:Q4_K_M

Use Docker

docker model run hf.co/ramankrishna10/npc-reason:Q4_K_M

LM Studio
Jan

vLLM

How to use ramankrishna10/npc-reason with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ramankrishna10/npc-reason"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ramankrishna10/npc-reason",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ramankrishna10/npc-reason:Q4_K_M

Ollama
How to use ramankrishna10/npc-reason with Ollama:
```
ollama run hf.co/ramankrishna10/npc-reason:Q4_K_M
```

Unsloth Studio

How to use ramankrishna10/npc-reason 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 ramankrishna10/npc-reason 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 ramankrishna10/npc-reason to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ramankrishna10/npc-reason to start chatting

Atomic Chat new
Docker Model Runner
How to use ramankrishna10/npc-reason with Docker Model Runner:
```
docker model run hf.co/ramankrishna10/npc-reason:Q4_K_M
```

Lemonade

How to use ramankrishna10/npc-reason with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramankrishna10/npc-reason:Q4_K_M

Run and chat with the model

lemonade run user.npc-reason-Q4_K_M

List all available models

lemonade list

npc-reason

File size: 657 Bytes

1ba301a

{
  "seed": 20260615,
  "n_total": 500,
  "sources": [
    {
      "name": "openai/gsm8k",
      "config": "main",
      "split": "test",
      "n": 300,
      "selection": "Random(SEED).shuffle -> first N"
    },
    {
      "name": "HuggingFaceH4/MATH-500",
      "split": "test",
      "n": 200,
      "selection": "Random(SEED+1).shuffle -> first N"
    }
  ],
  "schema": [
    "id",
    "source",
    "problem",
    "gold",
    "level",
    "subject"
  ],
  "frozen": true,
  "note": "Evaluation only. Never training data. Decontaminate training against this.",
  "eval_set_sha256": "e1573cabaf44010555ef1138cbeda17bc43deb8121524cde23d94413a5d69753"
}