Instructions to use ramankrishna10/npc-reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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 — Pre-registration of lift criteria (FROZEN before any training) | |
| Committed in Dispatch 1, before any SFT / distillation / RL exists. Same discipline as | |
| BigBugAI v2: the success bars are fixed in advance so the eventual before/after delta means | |
| something. These bars are deltas/targets chosen on principle; they do NOT depend on the | |
| measured baseline values (recorded separately in `reports/baseline.md` / `BASELINE.lock`), | |
| so there is no possibility of tuning the bars to the baseline. | |
| The comparator for the primary bar is the base model's **FORMAT-PROMPT** number — the honest | |
| question is "did training add something that prompting the base alone could not?", not | |
| "did training beat a base that was never asked for the format?". | |
| ## Metrics (all from the frozen mechanical verifier, VERIFIER.lock) | |
| - **verifiable-rate** — fraction of chains that are mechanically VERIFIABLE | |
| (≥1 load-bearing `<<EXPR=RESULT>>` assertion, all assertions verify, final answer composes). | |
| - **accuracy** — fraction whose final answer equals gold (independent of verifiable). | |
| - **verified-and-correct** — fraction that are BOTH verifiable AND correct. This is the headline. | |
| ## Committed criteria | |
| 1. **Primary (lift).** SFT NPC Reason must raise **verified-and-correct** by | |
| **≥ +15 percentage points absolute** over the base model's **format-prompt** verified-and-correct rate. | |
| 2. **Verifiable-rate target.** NPC Reason must reach **verifiable-rate ≥ 90%**. The project's | |
| purpose is near-total mechanical grounding of load-bearing steps; a specialized model should | |
| emit the checkable format almost always. | |
| 3. **Accuracy guard (no regression trade).** NPC Reason **accuracy** must not regress by more | |
| than **5 percentage points** versus the base model's **format-prompt** accuracy. Verifiability | |
| must not be bought by sacrificing correctness — both axes matter. | |
| SFT passes Dispatch 2 only if (1) AND (2) AND (3) all hold. | |
| 4. **RL gating (later dispatch).** The RL phase is gated on SFT meeting criteria 1–3. RL's own | |
| criteria will be pre-registered at that time, evaluated against a held-out slice never used | |
| for SFT model selection. The verifier (VERIFIER.lock) is the RL reward and must remain | |
| byte-identical. | |
| ## Honesty clause | |
| Report whatever the numbers say. A **null** — training fails to beat the format-prompt | |
| baseline on the primary bar — is a valid, publishable finding, consistent with this being a | |
| real experiment and not a foregone conclusion. The bars above are not moved after results are | |
| seen; if they are missed, that is the result. | |
| ## Frozen references | |
| - Verifier: `verifier/VERIFIER.lock` | |
| - Eval set: `eval/EVAL.lock` | |
| - Baseline: `baseline/BASELINE.lock` | |
| - This pre-registration: `reports/PREREG.lock` (sha256 of this file). | |