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If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️ > 💡 TL;DR > > The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama. 🔥 2024-07-29 : The Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English.
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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%.
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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This repository disseminates a comprehensive suite of 27 GGUF (GPT-Generated Unified Format) quantizations of **Muse-Glimmer-30B-Heretic-Uncensored**, an abliterated derivative of Meta's Muse-Glimmer-30B produced with Heretic v1.4.0 by darkc0de. The release spans a memory footprint of 6.53 GB (IQ1_S) to 55.73 GB (F16/BF16), thereby accommodating deployment across heterogeneous hardware, from CPU-only systems to 32 GB VRAM accelerators. All sub-4-bit quantizations were calibrated with an importance matrix (imatrix) to mitigate quality degradation. The fidelity of the Q4_K_S variant relative to the F16 reference is quantified via token-level perplexity on a fixed evaluation set. The model exhibits **zero refusals on adult/NSFW content**; this property was empirically verified. The quantization selection is derived from the model's architectural properties and the memory constraints of representative target hardware. The model employs **grouped-query attention with 2 KV heads**, which renders the KV cache exceptionally compact: - 52 layers × 2 KV heads × 128 head_dim = **52 KiB/token at FP16** - Q8_0 KV cache: **26 KiB/token** → 64K context ≈ 1.7 GB - Q4_0 KV cache: **13 KiB/token** → 64K context ≈ 0.85 GB
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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.
**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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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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