--- title: DeepSeek-TNG-R1T2-Chimera-GGUF canonical_url: "https://www.modelscope.ai/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF" md_url: "https://www.modelscope.ai/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF.md" repository: ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF last_updated: 2026-03-17 license: mit pipeline_tag: text-generation tasks: - text-generation base_model: - tngtech/DeepSeek-TNG-R1T2-Chimera base_model_relation: quantized library_name: - gguf - pytorch frameworks: - pytorch downloads: 197 stars: 0 tags: - mla - imatrix - conversational - ik_llama.cpp - gguf --- # DeepSeek-TNG-R1T2-Chimera-GGUF > DeepSeek-TNG-R1T2-Chimera-GGUF - An open-source model by ubergarm on ModelScope. ikllama.cpp imatrix Quantizations of DeepSeek-TNG-R1T2-Chimera This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not… ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF is a text-generation model on ModelScope. licensed under mit. derived from tngtech/DeepSeek-TNG-R1T2-Chimera. - **Repository**: ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF - **License**: mit - **Tasks**: text-generation - **Base model**: tngtech/DeepSeek-TNG-R1T2-Chimera - **Tags**: mla, imatrix, conversational, ik_llama.cpp, gguf - **Downloads**: 197 - **Stars**: 0 - **Last updated**: 2026-03-17 Source: https://www.modelscope.ai/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF --- ## `ik_llama.cpp` imatrix Quantizations of DeepSeek-TNG-R1T2-Chimera This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support the ik's latest SOTA quants and optimizations! Do **not** download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! *NOTE* `ik_llama.cpp` can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP. These quants provide best in class perplexity for the given memory footprint. ## Big Thanks Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!! Also thanks to all the folks in the quanting and inferencing community on [BeaverAI Club Discord](https://discord.com/channels/1238219753324281886/1238239819017097246/1238676202357784650) and on [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for tips and tricks helping each other run, test, and benchmark all the fun new models! ## Quants For some larger non-imatrix ik quant options check out [Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF](https://huggingface.co/Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF) #### * `IQ3_KS` 281.463 GiB (3.598 BPW) Special mix with all new `IQ3_KS` `ffn_(gate|up)_exps` and `IQ4_KS` `ffn_down_exps` routed experts. Mostly `iq5_ks/iq4_ks` for attn and shared expert. `iq5_k` `token_embd` and `iq6_k` `output` "head". Final estimate: PPL = 3.3167 +/- 0.01789
👈 Secret Recipe ```bash #!/usr/bin/env bash custom=" # First 3 dense layers (0-3) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[0-2]\.attn_k_b.*=q5_0 blk\.[0-2]\.attn_.*=iq5_ks blk\.[0-2]\.ffn_down.*=iq5_ks blk\.[0-2]\.ffn_(gate|up).*=iq4_ks blk\.[0-2]\..*=iq5_ks # All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[3-9]\.attn_k_b.*=q5_0 blk\.[1-5][0-9]\.attn_k_b.*=q5_0 blk\.60\.attn_k_b.*=q5_0 blk\.[3-9]\.attn_.*=iq5_ks blk\.[1-5][0-9]\.attn_.*=iq5_ks blk\.60\.attn_.*=iq5_ks # Shared Expert (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=iq5_ks blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq5_ks blk\.60\.ffn_down_shexp\.weight=iq5_ks blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks blk\.60\.ffn_(gate|up)_shexp\.weight=iq4_ks # Routed Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq4_ks blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq4_ks blk\.60\.ffn_down_exps\.weight=iq4_ks blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq3_ks blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq3_ks blk\.60\.ffn_(gate|up)_exps\.weight=iq3_ks # Token embedding and output tensors (GPU) token_embd\.weight=iq5_k output\.weight=iq6_k " custom=$( echo "$custom" | grep -v '^#' | \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ3_KS.gguf \ IQ3_KS \ 24 ```
#### * `IQ2_KS` 203.553 GiB (2.602 BPW) Special mix with `IQ2_KS` `ffn_(gate|up)_exps` and new `IQ3_KS` `ffn_down_exps` routed experts. Mostly `iq5_ks/iq4_ks` for attn and shared expert. `iq5_k` `token_embd` and `iq6_k` `output` "head". Final estimate: PPL = 3.6254 +/- 0.02001
👈 Secret Recipe ```bash #!/usr/bin/env bash custom=" # First 3 dense layers (0-3) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[0-2]\.attn_k_b.*=q5_0 blk\.[0-2]\.attn_.*=iq5_ks blk\.[0-2]\.ffn_down.*=iq5_ks blk\.[0-2]\.ffn_(gate|up).*=iq4_ks blk\.[0-2]\..*=iq5_ks # All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[3-9]\.attn_k_b.*=q5_0 blk\.[1-5][0-9]\.attn_k_b.*=q5_0 blk\.60\.attn_k_b.*=q5_0 blk\.[3-9]\.attn_.*=iq5_ks blk\.[1-5][0-9]\.attn_.*=iq5_ks blk\.60\.attn_.*=iq5_ks # Shared Expert (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=iq5_ks blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq5_ks blk\.60\.ffn_down_shexp\.weight=iq5_ks blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks blk\.60\.ffn_(gate|up)_shexp\.weight=iq4_ks # Routed Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq3_ks blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq3_ks blk\.60\.ffn_down_exps\.weight=iq3_ks blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_ks blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_ks blk\.60\.ffn_(gate|up)_exps\.weight=iq2_ks # Token embedding and output tensors (GPU) token_embd\.weight=iq5_k output\.weight=iq6_k " custom=$( echo "$custom" | grep -v '^#' | \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_KS.gguf \ IQ2_KS \ 24 ```
#### * `IQ2_KT` 171.146 GiB (2.188 BPW) Designed for RTX 6000 PRO Blackwell with 192GB total VRAM full offload with (hopefully) full 160k context and sufficiently large batch sizes. These `KT` quant types are quite fast on CUDA but not as fast TG on CPU inferencing. Special mix new trellis quants (like QTIP/EXL3 style) `IQ2_KT` `ffn_(gate|down|up)_exps` routed experts. Mostly `iq4_kt/iq3_kt` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head". Final estimate: PPL = 3.8887 +/- 0.02191
👈 Secret Recipe ```bash #!/usr/bin/env bash custom=" # First 3 dense layers (0-3) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[0-2]\.attn_k_b.*=iq4_nl blk\.[0-2]\.attn_.*=iq4_kt blk\.[0-2]\.ffn_down.*=iq4_kt blk\.[0-2]\.ffn_(gate|up).*=iq3_kt blk\.[0-2]\..*=iq4_kt # All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[3-9]\.attn_k_b.*=iq4_nl blk\.[1-5][0-9]\.attn_k_b.*=iq4_nl blk\.60\.attn_k_b.*=iq4_nl blk\.[3-9]\.attn_.*=iq4_kt blk\.[1-5][0-9]\.attn_.*=iq4_kt blk\.60\.attn_.*=iq4_kt # Shared Expert (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=iq4_kt blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_kt blk\.60\.ffn_down_shexp\.weight=iq4_kt blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_kt blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_kt blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_kt # Routed Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq2_kt blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq2_kt blk\.60\.ffn_down_exps\.weight=iq2_kt blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_kt blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_kt blk\.60\.ffn_(gate|up)_exps\.weight=iq2_kt # Token embedding and output tensors (GPU) token_embd\.weight=iq4_kt output\.weight=iq5_k " custom=$( echo "$custom" | grep -v '^#' | \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_KT.gguf \ IQ2_KT \ 24 ```
#### * `IQ2_XXS` 169.590 GiB (2.168 BPW) Not recommended, but should be faster and better quality than the IQ1_S and okay with full offload on multi-GPU. It should be okay for hybrid CPU+GPU inference as well if this size is good for your rig. Probably want to choose the IQ2_KT for full GPU offload. Special mix `IQ2_XXS` `ffn_(gate|up)_exps` and `IQ2_KS` `ffn_down_exps` routed experts. Mostly `iq4_ks/iq3_ks` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head". Final estimate: PPL = 4.0078 +/- 0.02291
👈 Secret Recipe ```bash #!/usr/bin/env bash custom=" # First 3 dense layers (0-3) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[0-2]\.attn_k_b.*=q4_0 blk\.[0-2]\.attn_.*=iq4_ks blk\.[0-2]\.ffn_down.*=iq4_ks blk\.[0-2]\.ffn_(gate|up).*=iq3_ks blk\.[0-2]\..*=iq4_ks # All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[3-9]\.attn_k_b.*=q4_0 blk\.[1-5][0-9]\.attn_k_b.*=q4_0 blk\.60\.attn_k_b.*=q4_0 blk\.[3-9]\.attn_.*=iq4_ks blk\.[1-5][0-9]\.attn_.*=iq4_ks blk\.60\.attn_.*=iq4_ks # Shared Expert (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=iq4_ks blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_ks blk\.60\.ffn_down_shexp\.weight=iq4_ks blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_ks # Routed Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq2_ks blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq2_ks blk\.60\.ffn_down_exps\.weight=iq2_ks blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_xxs blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_xxs blk\.60\.ffn_(gate|up)_exps\.weight=iq2_xxs # Token embedding and output tensors (GPU) token_embd\.weight=iq4_k output\.weight=iq5_k " custom=$( echo "$custom" | grep -v '^#' | \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_XXS.gguf \ IQ2_XXS \ 24 ```
#### * `IQ1_S` 132.915 GiB (1.699 BPW) Not recommended. "For the desperate". If you can fit a larger model in RAM+VRAM choose a larger model as it might even run faster and will definitely have better perplexity (likely better quality). Special mix `IQ1_S` `ffn_(gate|up)_exps` and `IQ1_M` `ffn_down_exps` routed experts. Mostly `iq4_ks/iq3_ks` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head". Final estimate: PPL = 4.9878 +/- 0.02999
👈 Secret Recipe ```bash #!/usr/bin/env bash custom=" # First 3 dense layers (0-3) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[0-2]\.attn_k_b.*=q4_0 blk\.[0-2]\.attn_.*=iq4_ks blk\.[0-2]\.ffn_down.*=iq4_ks blk\.[0-2]\.ffn_(gate|up).*=iq3_ks blk\.[0-2]\..*=iq4_ks # All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU) # Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 blk\.[3-9]\.attn_k_b.*=q4_0 blk\.[1-5][0-9]\.attn_k_b.*=q4_0 blk\.60\.attn_k_b.*=q4_0 blk\.[3-9]\.attn_.*=iq4_ks blk\.[1-5][0-9]\.attn_.*=iq4_ks blk\.60\.attn_.*=iq4_ks # Shared Expert (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=iq4_ks blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_ks blk\.60\.ffn_down_shexp\.weight=iq4_ks blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_ks # Routed Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq1_m blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq1_m blk\.60\.ffn_down_exps\.weight=iq1_m blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq1_s blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq1_s blk\.60\.ffn_(gate|up)_exps\.weight=iq1_s # Token embedding and output tensors (GPU) token_embd\.weight=iq4_k output\.weight=iq5_k " custom=$( echo "$custom" | grep -v '^#' | \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ1_S.gguf \ IQ1_S \ 24 ```
## Quick Start ``` ## clone latest ik_llama.cpp git clone https://github.com/ikawrakow/ik_llama.cpp.git cd ik_llama.cpp ## build for hybrid CUDA and CPU DeepSeek inferencing # apt-get install build-essential cmake ccache nvidia-cuda-toolkit # plus anything you need cmake -B ./build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_BLAS=OFF -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_IQK_FORCE_BF16=1 cmake --build ./build --config Release -j $(nproc) ## Run api server ./build/bin/llama-server \ --model /models/DeepSeek-TNG-R1T2-Chimera-IQ3_KS-00001-of-00007.gguf \ --alias ubergarm/DeepSeek-TNG-R1T2-Chimera-IQ3_KS \ -fa \ -mla 3 -fmoe -amb 512 \ --ctx-size 32768 \ -ctk q8_0 \ -ngl 99 \ -ot "blk\.(3|4)\.ffn_.*=CUDA0" \ -ot exps=CPU \ -ub 1024 -b 2048 \ --parallel 1 \ --threads 16 \ --host 127.0.0.1 \ --port 8080 ``` Adjust `--threads` to be equal to number of physical cores. Refer to various discussions on my other models for multi-NUMA, dual socket, and varying `--threads` and `--threads-batch` for larger server rigs. If you OOM on VRAM, remove the additional `-ot "...=CUDA0"` or you can increase offload layers if you have more VRAM with multi-GPU targets e.g. `-ot "blk\.(5|6)\.ffn_.*=CUDA1" \`. Test out `-rtr` to run-time-repack tensors to `_r4` variants layers when running on CPU/RAM likely faster in default ubatch sizes. Note this disables mmap() so will need enough RAM to malloc all the non-offloaded weights on startup. Generally `-ub 2048 -b 2048` or `-ub 4096 -b 4096` can give *much* faster PP speeds at the cost of some additional VRAM. Test against leavng it at the default `-ub 512 -b 2048`. Use `llama-sweep-bench --warmup-batch ...` to benchmark various configurations with your hardware to report to the community! ## TODO - [ ] Given the `IQ1_S_R4` is not symmetric with `IQ1_S` it doesn't work with `-rtr` so I might look into releasing an `_R4` variant after some `llama-sweep-bench` testing. - [ ] Consider a slightly larger model? (gotta free up some disk space lol) ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp) * [Larger ik quants available here: Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF](https://huggingface.co/Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF) * [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)