Instructions to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

SGLang

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Ollama:
```
ollama run hf.co/llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M
```
Unsloth Desktop

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M
```

Lemonade

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨

I can no longer upload new models unless I can cover the cost of additional storage.
I host 70+ free models as an independent contributor and this work is unpaid.
Without your support, no more new models can be uploaded.

☕ Ko-fi

Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.

91% fewer refusals (9/100 Uncensored vs 99/100 Original) while preserving model quality (0.0157 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

Platform	Link	What you get
☕ Ko-fi	Coffee Tips	My eternal gratitude

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.

GGUF quantizations of llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved

This is a decensored version of meituan-longcat/LongCat-Flash-Lite-Sparse, made using Heretic v1.4.0 with a variant of the Magnitude-Preserving Orthogonal Ablation (MPOA) method

Abliteration parameters

Parameter	Value
direction_index	per layer
attn.o_proj.max_weight	2.18
attn.o_proj.max_weight_position	9.23
attn.o_proj.min_weight	0.68
attn.o_proj.min_weight_distance	3.70
mlp.down_proj.max_weight	1.06
mlp.down_proj.max_weight_position	12.88
mlp.down_proj.min_weight	0.66
mlp.down_proj.min_weight_distance	5.69

Targeted components

attn.o_proj
mlp.down_proj

Performance

Metric	This model	Original model (LongCat-Flash-Lite-Sparse)
KL divergence	0.0157	0 (by definition)
Refusals	✅ 9/100	❌ 99/100

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections.

Quantizations

Filename	Quant	Description
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-BF16.gguf	BF16	Full precision
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q8_0.gguf	Q8_0	Near-lossless, recommended
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q6_K.gguf	Q6_K	Excellent quality
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q5_K_M.gguf	Q5_K_M	Good balance
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q5_K_S.gguf	Q5_K_S	Smaller Q5
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q4_K_M.gguf	Q4_K_M	Good for limited VRAM
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q4_K_S.gguf	Q4_K_S	Smaller Q4
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q3_K_L.gguf	Q3_K_L	Low VRAM, decent quality
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q3_K_M.gguf	Q3_K_M	Low VRAM, smaller
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q3_K_S.gguf	Q3_K_S	Very Low VRAM
LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-Q2_K.gguf	Q2_K	Very Very Low VRAM, only use if you have no other options

Usage

Works with my fork of llama.cpp.

LongCat-Flash-Lite-Sparse

Tech Report 📄

Model Introduction

LongCat-Flash-Lite-Sparse is a non-thinking Mixture-of-Experts (MoE) model with 69B total parameters and approximately 3B activated parameters per token. Built on LongCat-Flash-Lite, it replaces dense MLA with LongCat Sparse Attention (LSA) and natively supports context lengths of up to 1M tokens. Compared with its dense predecessor, LongCat-Flash-Lite-Sparse improves long-context inference efficiency and agentic capabilities while preserving strong reasoning and general-knowledge performance.

Key Features

🌟 LongCat Sparse Attention (LSA)

LSA is a hardware-efficient sparse-attention framework that extends DeepSeek Sparse Attention (DSA) with three complementary indexing mechanisms:

Streaming-Aware Indexing (SI) reallocates part of the token-selection budget to a fixed sink and local sliding window while retaining dynamic sparse selection for the remaining tokens. This design converts fragmented KV access into more predictable, contiguous reads, improving HBM access efficiency and effective bandwidth.
Cross-Layer Indexing (CLI) exploits the stability of attention saliency across adjacent layers, allowing multiple consecutive layers to reuse the results of a single indexing pass at inference time. This capability is learned through cross-layer distillation during training.
Hierarchical Indexing (HI) adopts a coarse-to-fine, two-stage scoring scheme: it first recalls candidate blocks using coarse-grained scores and then performs fine-grained token selection within those blocks. By reducing the candidate space processed for each query, HI lowers indexing overhead and can be enabled at inference time without additional training.

Together, these mechanisms enable LongCat-Flash-Lite-Sparse to process long contexts substantially more efficiently without compromising model quality.

🌟 Native 1M-Token Context Support

To strengthen long-context capabilities, we update the long-context extension stage of LongCat-Flash-Lite with an enriched corpus and a progressive schedule that scales to 1,024K, enabling native support for context lengths up to 1M.

🌟 Strong Agentic Performance

LongCat-Flash-Lite-Sparse delivers stronger performance than its dense predecessor on agentic coding, search, and tool-use tasks.

Please refer to our technical report for details!

Evaluation Results

Standard Benchmarks

Benchmark	Lite-Dense	Lite-Sparse (w/o HI)	Lite-Sparse (w/ HI)
Agentic Coding
SWE-Bench Verified(acc)	54.40	68.20	65.20
SWE-Bench Pro(acc)	-	40.63	39.40
SWE-Bench Multilingual(acc)	38.10	59.33	56.00
TerminalBench 2.0(acc)	33.75	33.70	32.58
Agentic Tool Use
τ²-Telecom(avg@4)	72.80	95.18	96.05
VitaBench(avg@4)	7.00	21.67	20.42
MCP-Atlas	-	45.60	45.00
Agentic Search
BrowseComp(pass@1)	-	48.62	48.18
BrowseComp-zh(pass@1)	-	61.94	61.59
RWSearch(pass@1)	-	68.50	66.00
General Domains
MMLU(acc)	85.52	85.31	85.14
MMLU-Pro(acc)	78.29	79.24	78.68
CMMLU(acc)	82.48	84.25	84.51
C-Eval(acc)	86.55	85.76	85.71
Mathematical Reasoning
GPQA-Diamond(avg@16)	66.78	69.49	69.03
MATH500(acc)	96.80	95.80	96.80
AIME 2026(avg@32)	-	65.73	64.90
HMMT 2026 Feb(avg@32)	-	40.53	41.47
BeyondAIME(avg@10)	-	44.20	42.30
IMO AnswerBench(avg@4)	-	49.38	46.69

Note: Lite-Dense denotes the LongCat-Flash-Lite, with metrics sourced from its technical report. Lite-Sparse (w/o HI) and Lite-Sparse (w/ HI) denote the sparse variants evaluated without and with Hierarchical Indexing, respectively.

Long-Context Benchmarks

Capability Dimension	Benchmark	Lite-Sparse (w/o HI)	Lite-Sparse (w/ HI)
Foundational
Retrieval	MRCR (8-needle)	44.66	44.47
Aggregation	OOLong-Synth	38.42	37.88
Multi-step Reasoning	GraphWalks Extend	66.27	65.63
Application
Question Answering	LOFT Retrieval Extend	43.75	44.38
In-context Learning	HELMET-ICL Extend	91.63	90.50
Code Understanding	LongCodeQA	62.30	59.37
Long-range Memory	AMemBench-ACU	33.25	33.13
Holistic Assessment	LongBench-v2	52.50	53.64
Holistic Assessment	AA-LCR	48.00	47.33

Deployment

We have implemented basic adaptations in SGLang (PR) to support the deployment of LongCat-Flash-Lite-Sparse.

LongCat-Flash-Lite-Sparse can be served on a single node (e.g., 1xH20-141G).

The server can be launched as follows:

python3 -m sglang.launch_server \
  --trust-remote-code \
  --model meituan-longcat/LongCat-Flash-Lite-Sparse \
  --host 0.0.0.0 \
  --port 30000 \
  --max-running-requests 64 \
  --mem-fraction-static 0.93 \
  --chunked-prefill-size 2048 \
  --nsa-prefill-backend fa3 \
  --kv-cache-dtype bfloat16

License Agreement

This repository, including both the model weights and the source code, is released under the MIT License.

Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.

For details, see the LICENSE file.

Usage Considerations

This model has not been specifically designed or comprehensively evaluated for every possible downstream application.

Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios. It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.

Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.

Contact

Please contact us at [email protected] or open an issue if you have any questions.

Downloads last month: 902

GGUF

Model size

69B params

Architecture

longcat-flash-sparse

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF

Base model

meituan-longcat/LongCat-Flash-Lite-Sparse

Finetuned

llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved

Quantized

(1)

this model

Papers for llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved-GGUF

LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing

Paper • 2608.01662 • Published Aug 3 • 4

Scaling Embeddings Outperforms Scaling Experts in Language Models

Paper • 2601.21204 • Published Jan 29 • 102