# IQuestLab/IQuest-Coder-V1

Repository: https://github.com/IQuestLab/IQuest-Coder-V1
Canonical: https://ross.abutalabs.com/products/iquest-coder-v1
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-03-02T11:58:44+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 70, release rhythm 35, longevity 17
- inputs: {"age_days": 245, "days_push": 184, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1389, forks 98 (observed 2026-08-28T04:04:35.404344+00:00)

## What it is
IQuest-Coder-V1 is a family of open code large language models (7B, 14B, and 40B variants including Base, Instruct, and Thinking versions) released on Hugging Face. The models are trained with a code-flow multi-stage paradigm and optimized for autonomous software engineering, tool use, CLI coding agents, and HTML/SVG generation with 128K context.

## Use cases
- run a local code LLM for code generation
- power a CLI coding agent like Claude Code or OpenCode with an open model
- generate HTML and SVG from natural language prompts
- fine-tune or build on top of open code model weights
- use a reasoning/thinking model for complex software engineering tasks
- self-host a code assistant with 128K context window

## When to choose
- you need open-weight code LLMs in 7B-40B sizes for local or self-hosted inference
- you want models specifically tuned for agentic coding and tool use
- you need long-context (128K) code understanding and generation
- you want both base weights for fine-tuning and instruct/thinking variants for direct use

## When to avoid
- you need a managed API service rather than self-hosted model weights
- you require a permissive license - the license is listed as NOASSERTION so verify terms first
- you need non-coding general-purpose chat models
- you lack GPU hardware to run 14B-40B parameter models

## Facets
- artifact type: dataset
- maturity: active
- function: llm-inference, machine-learning, agent-framework, prompt-engineering
- domain: large-language-models, artificial-intelligence, developer-tools
- platform: python, cross-platform
- tags: code-llm, model-weights, hugging-face, cli-agents, thinking-models, 128k-context, code-generation, ai-agents, gpu

## Member repositories
- IQuestLab/IQuest-Coder-V1 (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:35.404344+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:41.254295+00:00, confidence not recorded.
  - readme: https://github.com/IQuestLab/IQuest-Coder-V1 (fetched 2026-08-28T04:04:35.404344+00:00, sha 2212c3b0b7d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.