77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
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---
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license: apache-2.0
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language: [en]
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library_name: transformers
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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tags: [code, reasoning, think, local, npc, laconic]
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pipeline_tag: text-generation
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---
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# NPC Coder 1.5B
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A local-first coding agent with visible `<think>` reasoning, a laconic
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senior-engineer voice, and an honest-failure character (it flags uncertainty
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instead of inventing APIs). Built on Qwen2.5-Coder-1.5B-Instruct. Runs on a
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laptop in GGUF.
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## What it is
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- Visible step-by-step reasoning in `<think>` blocks before answering
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- Terse, here's-the-fix answers (no filler)
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- Admits uncertainty on hard or obscure problems rather than hallucinating
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- Stable NPC identity (does not claim to be Qwen)
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## Honest capability framing
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This is a 1.5B model. It handles easy-to-medium coding and debugging
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competently and reasons visibly about them. It is NOT an olympiad-level
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solver — on genuinely hard algorithmic problems the reasoning can be
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incomplete, and the model is trained to SAY so rather than emit
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confident-but-wrong solutions. Treat it as a fast local assistant for everyday
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coding, not a replacement for a frontier model on hard problems.
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It can still be overconfident on obscure *factual trivia* (exact default
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arguments, precise version numbers) — the honest-failure training mitigates
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but does not eliminate this at 1.5B. Verify specifics against the docs.
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**Benchmark:** HumanEval (instruct, pass@1, greedy): **65.9%**. Measured with
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`lm-eval-harness` `humaneval_instruct`. (The personality fine-tune slightly
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*improved* the extractable-code rate vs. the reasoning-only stage, because
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terser answers parse more cleanly.)
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## Personality behavior (held-out eval, 200 prompts)
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| behavior | result |
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|---|---|
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| Correct NPC identity when asked | 100% |
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| No identity mention on neutral coding (over-emission) | 2.5% |
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| Denies being Qwen / wrong maker | 100% |
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| Flags uncertainty on unknown/obscure APIs | 100% |
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## Training
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- **Stage 1 — reasoning:** SFT on `open-r1/codeforces-cots` (decontaminated
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Python subsets, fit-filtered to ≤8192 tokens so every `<think>` trace is
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complete; the filter biases toward shorter, laconic traces). 15k traces.
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- **Stage 2 — voice + identity + honest-failure:** SFT with a 7k-example
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personality set (gated identity, a large anti-over-emission cohort, an
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honest-failure cohort, and a 1k anti-forgetting buffer of Stage-1 reasoning
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data). LoRA, gentle LR, both stages merged.
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Apache 2.0 model. Reasoning data: `open-r1/codeforces-cots` (CC-BY-4.0 / ODC-By,
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attributed).
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## Local use
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GGUF quants: **q4_k_m (~941 MB, laptop default)**, q5_k_m (~1.1 GB), q8_0
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(~1.6 GB), f16 (~3.1 GB). At q4_k_m, ~7 tok/s on CPU. Uses the standard ChatML
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(`<|im_start|>` / `<|im_end|>`) template.
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If q4_k_m's coherence on edge cases matters to you, q5_k_m is a cleaner default.
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## Attribution & author
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Reasoning data: `open-r1/codeforces-cots` (HuggingFace Open-R1), CC-BY-4.0.
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Base model: `Qwen/Qwen2.5-Coder-1.5B-Instruct`, Apache 2.0.
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Author: Rama Krishna Bachu / Bottensor (Independent Research). ORCID 0009-0000-1298-0681.
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