Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. Key features: - Excelling in long-context understanding with a maximum context length of 128K tokens. - Supporting 52 major programming languages: As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
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Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. Key features: - Excelling in long-context understanding with a maximum context length of 128K tokens. - Supporting 52 major programming languages: As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
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**RVN** is a double-refined abliterated variant of **Qwen3.8-27B**, built on top of `trohrbaugh/Qwen3.8-27B-heretic-ara` (an ARA abliteration by **Tim Rohrbaugh**) and further refined with **two additional full-weight ARA passes** targeting residual refusals. It retains very low behavioral damage (KL ≈ 0.0085) while reducing harmful-prompt refusals from 3/100 (source) to **0–1/100** in independent measurements. > **Note on this repository's history.** This repo previously hosted the original > `Qwen3.8-27B-Heretic-Q4_K_M.gguf` (single-quant release from the earlier > `trohrbaugh/Qwen3.8-27B-heretic` source). That file is kept as **legacy** for > download-count continuity and backward compatibility — it is the *older* abliteration > variant and is superseded by the RVN files below. Prefer the RVN quants for new > deployments. > **Not for all audiences.** This model has reduced safety guardrails by design. It is > intended for adult audiences (18+) doing research, creative writing, roleplay, and > uncensored generation. Certain guardrails are intentionally left in place; use > responsibly and in accordance with your local laws.
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**Non-uniform GGUF quantizations** of **RVN Qwen3.8-27B-Heretic-Abliterated-Uncensored** (refusals 0-1/100, KL ~ 0.0085 vs base). Instead of one uniform quantization type for all tensors, **each of the 851 tensors carries its own type**, following the per-tensor **RCO allocation** published by IST-DASLab for their GSQ-RCO release of Qwen3.8-27B, quantized locally with **llama.cpp** and the **official importance matrix** `imatrix-qwen3.8-27b.gguf`. Files are standard GGUF and run unmodified in llama.cpp / Ollama / LM Studio. This family was made at the request of community member **Heidagger44** in discussion #11: > Regarding IQ3_S there is new method: GSQ-RCO, if it's possible to combine your abliteration method with this quantization, we would have a true refusal free Qwen3.8-27B running fully on 16GB cards.
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Split GGUF builds of `0bserverx/RVN-Qwen3.8-Flash-Next-Abliterated-Uncensored`. This repository currently publishes 18 verified quant families as 144 GGUF shards. Five IQ families were produced with the V6 calibration importance matrix; the remaining families are standard core quantizations. - Text/main model only. - NextN/MTP speculative-draft tensors excluded. - Vision projector excluded. - Produced from the verified F16 V6 main checkpoint. - Files are split below the Hugging Face per-file limit. - A recent `llama.cpp` build with `qwen4exp` support is required.
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Our pick an **experimental (advanced) mode** at this model a continuously trained `Qwen2.5-Coder-0.5B-Instruct` fine-tuned using **Gensyn RL-Swarm** framework with **GRPO (Group Relative Policy Optimization)** and supported format **GGUF (llama.cpp)** for enhanced code generation capabilities. **Note: Current training focuses on programming challenges with adaptive weighted sampling**. **Total Dataset Size:** Streaming datasets with infinite iteration **Training Samples per Round:** 2 **Evaluation:** Real-time via Swarm Coordination with Ollama-based evaluator else Judge > "When the solvers perform well, the proposer automatically increases the difficulty to keep challenging solvers to get better over time." - CodeZero-blog
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> [!IMPORTANT] > **Generation is validated on one physical NVIDIA DGX Spark.** Use the pinned > Docker recipe below. Stock vLLM, stock ExLlamaV3, and a bare > `--tensor-parallel-size 1` command cannot load this TP4 rank-sliced artifact > correctly. This is a REAP-pruned, 3.0 bpw EXL3/Trellis build of `deepseek-ai/DeepSeek-V4-Flash-0731`, packaged for a **single 128 GB DGX Spark** through `local-inference-lab/sparkinfer`. - **Runtime repository:** `0xSero/deepseek-v4-flash-0731-spark-sparkinfer` - **Pinned public image:** `ghcr.io/0xsero/deepseek-v4-flash-0731-spark-sparkinfer@sha256:2e077489a83a0360952828051fe7f7a32c1801e5ce8436d85f7267583d614ff4` - **Pinned model revision:** `22f28d32b9b29b4352eaa380ff8c2c170b2847ab` - **API:** OpenAI-compatible server on `http://127.0.0.1:8000` - **Configured model limit:** 262,144 tokens
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