Instructions to use ramankrishna10/npc-reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 — Dispatch 1: Verifier + Frozen Baseline ===
Verifier: mechanical <<EXPR=RESULT>> checker (SymPy), pure-code, no LLM judgment. VERIFIER.lock d5d146cf… (also the later RL reward signal). tests: correct / arithmetic-error / fluency-trap / non-composing / verifiable-but-wrong / correct-but-unverifiable / variable-binding / unbound-fail-closed / tolerance(exact+1e-6) / extraction — 14/14 pass. Eval: 500 problems = 300 GSM8K(test) + 200 MATH-500, held-out & frozen. EVAL.lock e1573cab… ; meta records sources/splits/seed(20260615). Never training data. Baseline: DeepSeek-R1-Distill-Qwen-1.5B (MIT), untouched, greedy/deterministic (seed 0, max_tokens 12288, ctx 16384). 1000 chains in 556s. BASELINE.lock frozen.
plain-prompt format-prompt
verifiable-rate 0.0% 0.0% <-- base never emits <<...>> (1/500 contain "<<")
accuracy 59.8% 61.6% (floor; greedy repetition leaves 83/500 fmt no-answer)
verified-and-correct 0.0% 0.0%
mean assertions/chain 0.0 0.0
Reading: The base does NOT produce mechanically-checkable steps, even when explicitly asked
(format-prompt verifiable-rate is 0%, not just plain). The project's headline delta
is therefore real and large-headroom: any verifiable-rate NPC Reason reaches is lift
over 0. Accuracy is a separate axis (60%) and is the guard, not the goal.
Prereg: FROZEN before training (PREREG.lock b5a49437…). Bars committed: 1. primary: verified-and-correct +>=15pp over format baseline (i.e. >=15%) 2. verifiable-rate >= 90% 3. accuracy must not regress > 5pp below format accuracy 61.6% (i.e. >= 56.6%) 4. RL gated on SFT passing 1-3; RL criteria pre-registered later. Honesty clause: a null (training fails to beat format baseline) is a valid finding.
Next: Dispatch 2 = generate DeepSeek-V4 verifiable <<...>> chains, code-filter against VERIFIER.lock, SFT-distill into R1-Distill-1.5B; decontaminate against EVAL.lock. GPU: inference only; A40 ~4.5GB peak. No training performed this dispatch.
Notes: - Re-ran baseline at 12288 tokens (from 4096) to remove a truncation artifact; 4096 run archived at baseline/baseline_results.trunc4096.json. verifiable-rate 0% in BOTH runs (robust). Verifier frozen BEFORE both baseline runs (not tuned to them). - R1-Distill greedy repetition (vendor recommends temp 0.6) leaves ~17% of chains without a final answer; counted wrong, so accuracy is a floor. Same decoding will apply post-training -> apples-to-apples.