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 / reports /dispatch4_complete.md

ramankrishna10

verifier + eval harness + reports + locks + usage

1ba301a verified about 1 month ago

preview code

Raw

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3.65 kB

=== NPC Reason - Dispatch 4: Packaging + Card ===

GGUF: f16 / q8_0 / q4_K_M of the shipped RL model (rl/merged), built with llama.cpp (CUDA). Decision-fidelity vs bf16 on 100 frozen-eval problems (greedy, frozen verifier d5d146cf): f16 dVC +0.0 OK q8_0 dVC +0.0 OK <- RECOMMENDED (behaviorally lossless, 1.9 GB) q4_K_M dVC +6.0 DRIFTS (exceeds +/-2pp) -> flagged, NOT recommended The Fin Nano lesson reproduced: q4 is structurally valid but changes decisions under quant noise. Do not pick a quant on file size alone. (reports/gguf_fidelity.md)

Card: MODEL_CARD.md - full base/SFT/RL arc (verifiable 0 -> 76.8 -> 76.2; v&c 0 -> 58.0 -> 59.6; accuracy 61.6 -> 65.8 -> 66.6), distillation-did-the-heavy-lifting, RLVR-stable-but-small (~tied with SFT), 90% verifiable bar NOT met (stated as the open limitation), verifier d5d146cf documented as BOTH the metric AND the reward, MIT/V4 lineage, attribution Rama Krishna Bachu / Bottensor / ORCID 0009-0000-1298-0681. No overclaiming, no em dashes. USAGE.md - format prompt + vLLM/GGUF run + how to run the verifier on outputs.

Release tree (release/, 22 curated text files; model + GGUF binaries referenced separately): MODEL_CARD.md, USAGE.md verifier/ (step_verifier.py + VERIFIER.lock) <- the product: users verify chains eval/ (run_eval.py, eval_set.jsonl, _eval_meta.json, EVAL.lock) tests/ (test_verifier.py) prereg/ (PREREG.md + PREREG.lock) locks/ (BASELINE / TRAINSET / SFT_RESULT / RL_RESULT .lock) reports/ (baseline, sft_result, dispatch1/2/3_complete, gguf_fidelity, fidelity_result.json) MODEL BINARIES (push-time): rl/merged/ (bf16, shipped) + gguf/{f16,q8_0,q4_K_M}. EXCLUDED (de-link / methodology / secrets-touching): decisions.md, data/generate_v4.py (DeepSeek API code), all *.sh run/build scripts, raw logs, reward_stats, training scripts.

Credential scan: CLEAN (HARD-HALT gate PASSED). Adversarial multi-angle audit (5 independent auditors + reconciling synthesizer, all CLEAN, independently re-verified on the host): - strict secret regex (sk-/hf_/ghp_/gho_/AKIA/xox*/AIza/BEGIN PRIVATE KEY): zero matches. - all 5 known chat-leaked key prefixes (DeepSeek / HF x2 / CoinGecko / OpenAI-style): absent. - "DEEPSEEK_API_KEY" string: absent from release/ (only in excluded generate_v4.py + decisions.md). - release .py files: local model-load + SymPy only, no os.environ/network/auth. - false-positives dismissed: SHA-256 hashes, env-var NAMES, tokenizer vocab tokens, a GSM8K "secret seasoning" word problem.

Push: NOT done (prep only, per dispatch). Preconditions before a push dispatch runs: 1. Operator supplies HF org/repo name and confirms PRIVATE. 2. Operator ROTATES the DeepSeek key (still outstanding; it passed through chat). 3. At push time, re-confirm a FULL (not sampled) credential scan against the EXACT push manifest IF model binaries (gguf/safetensors) are bundled (they live outside release/ and were sampled-clean: safetensors header no-secret, gguf strings clean). 4. Apply the same de-link discipline used for the BigBugAI HF push (no lineage docs).

Decisions np-015..np-018 logged. 7 frozen locks intact (no training/eval-logic changes this dispatch). Project complete: base 0% -> SFT 76.8% -> RL 76.2%/59.6% v&c, verifier-grounded, honestly reported.