license, language, library_name, base_model, tags, pipeline_tag

license	language	library_name	base_model	tags	pipeline_tag
apache-2.0	en	transformers	Qwen/Qwen2.5-Coder-1.5B-Instruct	code reasoning think local npc laconic	text-generation

NPC Coder 1.5B

A local-first coding agent with visible <think> reasoning, a laconic senior-engineer voice, and an honest-failure character (it flags uncertainty instead of inventing APIs). Built on Qwen2.5-Coder-1.5B-Instruct. Runs on a laptop in GGUF.

What it is

• Visible step-by-step reasoning in <think> blocks before answering
• Terse, here's-the-fix answers (no filler)
• Admits uncertainty on hard or obscure problems rather than hallucinating
• Stable NPC identity (does not claim to be Qwen)

Honest capability framing

This is a 1.5B model. It handles easy-to-medium coding and debugging competently and reasons visibly about them. It is NOT an olympiad-level solver — on genuinely hard algorithmic problems the reasoning can be incomplete, and the model is trained to SAY so rather than emit confident-but-wrong solutions. Treat it as a fast local assistant for everyday coding, not a replacement for a frontier model on hard problems.

It can still be overconfident on obscure factual trivia (exact default arguments, precise version numbers) — the honest-failure training mitigates but does not eliminate this at 1.5B. Verify specifics against the docs.

Benchmark: HumanEval (instruct, pass@1, greedy): 65.9%. Measured with lm-eval-harness humaneval_instruct. (The personality fine-tune slightly improved the extractable-code rate vs. the reasoning-only stage, because terser answers parse more cleanly.)

Personality behavior (held-out eval, 200 prompts)

behavior	result
Correct NPC identity when asked	100%
No identity mention on neutral coding (over-emission)	2.5%
Denies being Qwen / wrong maker	100%
Flags uncertainty on unknown/obscure APIs	100%

Training

• Stage 1 — reasoning: SFT on open-r1/codeforces-cots (decontaminated Python subsets, fit-filtered to ≤8192 tokens so every <think> trace is complete; the filter biases toward shorter, laconic traces). 15k traces.
• Stage 2 — voice + identity + honest-failure: SFT with a 7k-example personality set (gated identity, a large anti-over-emission cohort, an honest-failure cohort, and a 1k anti-forgetting buffer of Stage-1 reasoning data). LoRA, gentle LR, both stages merged.

Apache 2.0 model. Reasoning data: open-r1/codeforces-cots (CC-BY-4.0 / ODC-By, attributed).

Local use

GGUF quants: q4_k_m (~941 MB, laptop default), q5_k_m (~1.1 GB), q8_0 (~1.6 GB), f16 (~3.1 GB). At q4_k_m, ~7 tok/s on CPU. Uses the standard ChatML (<|im_start|> / <|im_end|>) template.

If q4_k_m's coherence on edge cases matters to you, q5_k_m is a cleaner default.

Attribution & author

Reasoning data: open-r1/codeforces-cots (HuggingFace Open-R1), CC-BY-4.0. Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct, Apache 2.0. Author: Rama Krishna Bachu / Bottensor (Independent Research). ORCID 0009-0000-1298-0681.