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
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.
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.
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.
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).