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Model: ramankrishna10/npc-coder-1.5b-gguf
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language: [en]
+library_name: transformers
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+tags: [code, reasoning, think, local, npc, laconic]
+pipeline_tag: 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.