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.
Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.
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.
These GGUFs only works with my fork, which you can get from here: llama.cpp, these GGUFs will NOT work on upstream llama.cpp.
And then to load the model use something like this example:
GGUF quantizations of llmfan46/LongCat-Flash-Lite-Sparse-Uncensored-Heretic-Native-MTP-And-LSA-Preserved
| 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 |
| 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.
| 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 |
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.
LSA is a hardware-efficient sparse-attention framework that extends DeepSeek Sparse Attention (DSA) with three complementary indexing mechanisms:
Together, these mechanisms enable LongCat-Flash-Lite-Sparse to process long contexts substantially more efficiently without compromising model quality.
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.
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!
| 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.
| 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 |
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
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.
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.
Please contact us at longcat-team@meituan.com or open an issue if you have any questions.