Kimi-K3-Abliterated-modal

1. Model Introduction

SHS-Lab/Kimi-K3-Abliterated is an open-weight, natively multimodal agentic model derived from Kimi K3 through a targeted abliteration procedure. The base architecture is a 2.8-trillion-parameter Mixture-of-Experts network built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), featuring native vision capabilities and a 1-million-token context window.

This variant has been post-processed to attenuate alignment-driven refusal mechanisms. The underlying architecture, parameter count, context length, and multimodal capabilities remain identical to the base model. Abliteration modifies weight values to suppress safeguard activations without altering the structural design or introducing new training data.

The release is intended for alignment research, red-team evaluation, and controlled experimentation in governed environments.

Key Features

• Abliterated Behavior: Refusal pathways have been attenuated through directed weight surgery, reducing the probability of alignment-triggered refusals for English and Japanese inputs. More than 98 % of identified safeguard signal has been removed across targeted weight families.
• Long-Horizon Coding: Supports sustained engineering sessions across large codebases, terminal-tool orchestration, GPU kernel work, compiler development, and vision-in-the-loop workflows — inherited from the base model's agentic training.
• Agentic Knowledge Work: Capable of end-to-end research pipelines including interactive visualization generation, document processing, spreadsheet manipulation, and multi-step web browsing via MCP tool integration.
• Native Multimodality & Long Context: Processes text, images, and video within a single architecture. Supports a 1-million-token context window for full-repository ingestion and extended multi-turn sessions.
• Structured Reasoning: Thinking is always enabled. The model emits explicit reasoning_content traces with configurable effort levels (low, high, max).
• Open Frontier Weights: Distributed under the Kimi K3 License for research, self-hosted deployment, and downstream modification.
• Quantized Inference: Ships with MXFP4 weights and MXFP8 activations from quantization-aware training, enabling broad hardware compatibility.

2. Model Summary

All architectural specifications are inherited from the base Kimi K3 model. Abliteration modifies weight values but does not change any structural hyperparameter.

3. Abliteration Details

3.1 Methodology

Abliteration is a post-training weight-editing technique that locates and suppresses the internal activation directions responsible for alignment-induced refusal. Unlike gradient-based fine-tuning, abliteration operates directly on weight tensors: it identifies the subspace most correlated with refusal behavior and projects it out of each targeted layer.

The procedure requires no additional training data, no gradient computation, and no access to the original training pipeline. It is applied post-hoc to the released base-model weights.

3.2 Scope and Removal Rates

This model has been processed to reduce refusal likelihood for English and Japanese inputs. More than 98 % of the identified safeguard signal has been attenuated across all targeted weight families. The per-component removal rates are summarized below.

3.3 Behavioral Characteristics

Relative to the base Kimi K3 release, this variant:

• Produces substantially fewer refusal responses to prompts that would normally trigger alignment safeguards.
• Retains the base model's reasoning, coding, agentic, and multimodal capabilities without architectural modification.
• Does not introduce new knowledge, training data, or capabilities beyond what the base model provides.
• May exhibit minor distributional shifts in tone or phrasing as a side effect of weight projection.

3.4 Intended Use

This release is intended for:

• Alignment and safety research, including refusal-mechanism analysis and robustness testing.
• Red-team evaluation in controlled settings.
• Academic and industrial experimentation where unrestricted model behavior is required and appropriately governed.

This model is not intended for unsupervised deployment in consumer-facing products or in contexts where content moderation is a regulatory requirement.

4. Evaluation Results

[!IMPORTANT] All benchmark scores in this section were reported for the base Kimi K3 model by its original developers. No independent evaluation campaign has been conducted on this abliterated variant. These results are reproduced as an upper-bound reference for inherited capability. Abliteration targets refusal behavior and is not expected to materially alter benchmark performance, but users should validate task-specific accuracy independently before relying on this variant in production or research settings.

All Kimi K3 results are obtained with reasoning effort set to max and temperature = 1.0. For single-step tasks, such as GPQA Diamond, HLE-Full, and vision benchmarks without tools, top-p = 0.95; for agentic tasks, top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision, and ZeroBench, each cell reports the scores without and with tool augmentation (general tools for HLE-Full, Python for the vision benchmarks), in that order.

Reasoning & knowledge benchmarks

• CritPt and AA-LCR. Scores are cited from Artificial Analysis as of July 23, 2026.

Coding benchmarks

• DeepSWE. Kimi K3 is evaluated with the Kimi Code harness. The GLM-5.2 score is taken from the GLM-5.2 release blog; all remaining scores are from the official DeepSWE leaderboard, under which Kimi K3 attains 67.3 with the mini-SWE-agent harness. We report the DeepSWE v1.1 tasks.
• Terminal-Bench 2.1. Kimi K3 is evaluated with the Kimi Code harness. For all other models, we report the best score across harnesses: GLM-5.2 with Claude Code (GLM-5.2 release blog); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI).
• ProgramBench. Kimi K3 is evaluated with the Kimi Code harness. The GLM-5.2 score is from the GLM-5.2 release blog; all other scores are from Vals AI.
• SWE-Marathon. Kimi K3, Claude Opus 4.8, and Claude Fable 5 are evaluated with the Claude Code harness; GPT-5.6 Sol is evaluated with the Codex harness. The GLM-5.2 score is from the GLM-5.2 release blog. Our evaluation is based on an H20-calibrated branch of the official tasks as of July 9, 2026, prior to the final v1.1 release. Additionally, Claude Fable 5 hit fallbacks on 35% of the tasks in our evaluation, which may have negatively impacted its measured performance.
• FrontierSWE. Kimi K3 is evaluated with the Kimi Code harness and GPT-5.6 Sol with the Codex harness; all other results are from FrontierSWE. Dominance scores are recomputed from the raw scores using the official evaluation script and are current as of July 16, 2026.
• PostTrainBench. Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the official PostTrainBench results. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are evaluated with the official Harbor implementation at maximum reasoning effort, averaged over three runs on H20 GPUs — Kimi K3 and Claude Fable 5 with the Claude Code harness, and GPT-5.6 Sol with the Codex harness.
• MLS-Bench-Lite. Kimi K3 is evaluated with the Kimi Code harness; GLM-5.2 and the Claude models with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol with the Codex harness.
• SciCode. Scores are cited from Artificial Analysis as of July 23, 2026.
• Kimi Code Bench 2.0 (in-house). Kimi K3 is evaluated with the Kimi Code harness (it attains 73.7 with the Claude Code harness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol with the Codex harness. All models are evaluated at maximum reasoning effort, except GPT-5.5, which uses the "xhigh" setting. As the benchmark includes cybersecurity and safety-related tasks, we also disclose the fraction of refused or fallback tasks: Claude Fable 5 hit 13 fallbacks and 1 refusal out of 80 tasks; 10 refusals out of 80 tasks entered GPT-5.6 Sol's cyber guard; GPT-5.5 had 3 refusals out of 80 tasks.

Agentic benchmarks

• OfficeQA Pro. Each test case provides the agent with the entire PDF corpus, with all PDFs rendered as images and no machine-readable text available.
• OfficeQA Pro and SpreadsheetBench 2. Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are evaluated with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol are evaluated with the Codex harness.
• MCP-Atlas. All models are evaluated on the 500-task public subset with a 100-turn limit, using Gemini 3.1 Pro as the judge.
• AutomationBench. All models are evaluated on the 600-task public subset, following the official GitHub setup in all other respects.
• BrowseComp. We adopt a context-compaction strategy triggered at 300K tokens. When evaluated with the full 1M-token context window and no context management, Kimi K3 achieves a score of 90.4. The results of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from Anthropic and OpenAI.
• GDPval-AA v2, AA-Briefcase, τ³-Banking, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the APEX-Agents leaderboard as of July 23, 2026. For Harvey Lab-AA, we report the criterion pass rate.
• CorpFin v2, Finance Agent v2, and Legal Research Bench. Scores are cited from Vals AI.
• Agents' Last Exam. Scores are cited from the official leaderboard as of July 23, 2026; we report the leaderboard's primary pass-rate metric. On the leaderboard, each model is paired with a specific harness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. † The Claude Fable 5 entry runs at xhigh effort with 40% of tasks annotated as downgraded.

Multimodal benchmarks

• Except for ZeroBench, which follows the official setting and is run five times, all multimodal scores are averaged over three runs. MMMU-Pro is evaluated following the official protocol, preserving the original input order and prepending images to the text input.
• PerceptionBench is an in-house benchmark that focuses on atomic visual perception capabilities.

5. Native MXFP4 Quantization

This model inherits the quantization-aware training (QAT) configuration from the base Kimi K3 release. Quantization-aware training is applied from the SFT stage onward, using MXFP4 weights with MXFP8 activations for broad hardware compatibility. No separate post-training quantization step is required.

6. Deployment

[!NOTE] SHS-Lab/Kimi-K3-Abliterated is an open-weight release intended for self-hosted deployment. There is no hosted API provided by SHS-Lab. The following inference engines are recommended for serving this model.

The model is compatible with the following inference engines:

• vLLM — see serving recipes
• SGLang — see cookbook
• TokenSpeed — see recipes

[!NOTE] The engine references above point to the base-model serving guides. Configuration parameters (tensor parallelism, quantization flags, context length, expert parallelism) apply identically to this abliterated variant. Consult each engine's documentation for hardware-specific tuning.

Example: Serving with vLLM

vllm serve SHS-Lab/Kimi-K3-Abliterated \
    --tensor-parallel-size 8 \
    --trust-remote-code \
    --max-model-len 1,048,576 \
    --enable-auto-tool-choice \
    --tool-call-parser kimi_k3

7. Model Usage

Thinking and Reasoning Effort

Thinking is always enabled. The model returns a reasoning_content field alongside the standard content response. Reasoning effort is controlled via the top-level reasoning_effort request parameter:

Multi-Turn Conversations

The base model was trained in preserved thinking history mode. In multi-turn or tool-use sessions, pass the complete assistant message — including reasoning_content and tool_calls — back into messages. Omitting the reasoning trace degrades coherence across turns.

OpenAI-Compatible API

import openai

client = openai.OpenAI(
    base_url="http://localhost:8000/v1",  # adjust to your serving endpoint
    api_key="EMPTY",
)

def chat_with_preserved_thinking(client: openai.OpenAI, model_name: str):
    messages = [
        {
            "role": "user",
            "content": "Tell me three random numbers.",
        },
        {
            "role": "assistant",
            "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.",
            "content": "473, 921, 235",
        },
        {
            "role": "user",
            "content": "What are the other two numbers you have in mind?",
        },
    ]

    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        max_tokens=4096,
        reasoning_effort="max",
    )

    print(f"reasoning: {response.choices[0].message.reasoning_content}")
    return response.choices[0].message.content

result = chat_with_preserved_thinking(client, "SHS-Lab/Kimi-K3-Abliterated")
print(f"content: {result}")

Local Inference with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "SHS-Lab/Kimi-K3-Abliterated"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Summarize the key differences between KDA and standard multi-head attention."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=1.0,
    top_p=0.95,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Vision Input

from transformers import AutoProcessor, AutoModelForImageTextToText

MODEL_ID = "SHS-Lab/Kimi-K3-Abliterated"

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://example.com/diagram.png"},
            {"type": "text", "text": "Describe the architecture shown in this diagram."},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024)
print(processor.decode(outputs[0], skip_special_tokens=True))

Coding Agent Framework

This model is compatible with agentic coding frameworks that support the OpenAI-compatible chat completions API. Configure your preferred agent framework (e.g., Kimi Code CLI, OpenHands, Aider) to point at the serving endpoint and select SHS-Lab/Kimi-K3-Abliterated as the model identifier.

8. License

Both the model weights and any associated code are distributed under the Kimi K3 License, inherited from the base model. All terms, conditions, and restrictions of the original license apply to this derivative release. Users are responsible for ensuring compliance with the base license and any applicable local regulations before deployment.

9. Acknowledgements

This release is an independent derivative work produced by SHS-Lab. The base model architecture, pretrained weights, and original training were developed by Moonshot AI as part of the Kimi K3 project. We acknowledge the Kimi K3 team for releasing open frontier-scale weights and for the architectural innovations (KDA, AttnRes, Stable LatentMoE, MoonViT-V2) that make this derivative possible.

This repository is not affiliated with, endorsed by, or maintained by Moonshot AI. The abliteration procedure, documentation, and any variant-specific modifications are the sole responsibility of SHS-Lab.

Released by SHS-Lab for alignment research and controlled experimentation.