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AlexWortega/qwen35-4b-soyuz-grpo-gguf

🤗 Hugging Face sourceapache-2.04B activated11 GBGGUF✓ 4 checksumsupdated today
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Soyuz-4B GRPO — Q4_K_M GGUFs

llama.cpp Q4_K_M quants of the GRPO LoRA fine-tunes of Soyuz-4B, where the reward is a separate Claude Code instance grading each rollout. Each version is a merged LoRA → GGUF.

file version what it adds measured effect
v1.Q4_K_M.gguf v1 GRPO on general-coding tasks, free-form quality rubric flat learning (+0.004, noisy judge)
v2.Q4_K_M.gguf v2 + criteria-checklist reward + library/codebase-fix tasks clean gain (+0.045, 19/34 up); greedy base→GRPO +0.06
v3.Q4_K_M.gguf v3 + creative tasks + per-turn recovery reward (harsh grounded critique) raw +0.057, turn-0 +0.071 (recovery internalised into first-try); ⚠️ regresses on agentic terminal-bench (general-coding GRPO doesn't transfer to shell agents)
v5.Q4_K_M.gguf v5 + single-shot tasks + Hermes-agent tool-calling tasks best run: ALL +0.081, turn-0 +0.101; single-shot +0.208, hermes turn-0 +0.109 (learns the right tool / clarify / approval on the first try)

Serve

llama-server -m v3.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99 --jinja --ctx-size 32768

Best for single-response coding / writing / rewriting. For multi-step shell agents prefer the base Soyuz-4B. Suppress <think> for one-shot app/code generation (the model over-plans otherwise).

Details

Full GRPO recipe + honest effect analysis (incl. the negative agentic-transfer result) on the v3 model card: AlexWortega/qwen35-4b-soyuz-grpo-v3. W&B: https://wandb.ai/alexwortega/fast-qwen35-sft