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Model: bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF Source: Original Platform
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---
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quantized_by: bartowski
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pipeline_tag: text-generation
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base_model: SUFE-AIFLM-Lab/Fin-R1
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base_model_relation: quantized
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---
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## Llamacpp imatrix Quantizations of Fin-R1 by SUFE-AIFLM-Lab
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4944">b4944</a> for quantization.
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Original model: https://huggingface.co/SUFE-AIFLM-Lab/Fin-R1
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All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
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Run them in [LM Studio](https://lmstudio.ai/)
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Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
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## Prompt format
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```
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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## Download a file (not the whole branch) from below:
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| Filename | Quant type | File Size | Split | Description |
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| -------- | ---------- | --------- | ----- | ----------- |
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| [Fin-R1-bf16.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-bf16.gguf) | bf16 | 15.24GB | false | Full BF16 weights. |
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| [Fin-R1-Q8_0.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q8_0.gguf) | Q8_0 | 8.10GB | false | Extremely high quality, generally unneeded but max available quant. |
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| [Fin-R1-Q6_K_L.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q6_K_L.gguf) | Q6_K_L | 6.52GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
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| [Fin-R1-Q6_K.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q6_K.gguf) | Q6_K | 6.25GB | false | Very high quality, near perfect, *recommended*. |
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| [Fin-R1-Q5_K_L.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q5_K_L.gguf) | Q5_K_L | 5.78GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
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| [Fin-R1-Q5_K_M.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q5_K_M.gguf) | Q5_K_M | 5.44GB | false | High quality, *recommended*. |
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| [Fin-R1-Q5_K_S.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q5_K_S.gguf) | Q5_K_S | 5.32GB | false | High quality, *recommended*. |
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| [Fin-R1-Q4_K_L.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q4_K_L.gguf) | Q4_K_L | 5.09GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
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| [Fin-R1-Q4_1.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q4_1.gguf) | Q4_1 | 4.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
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| [Fin-R1-Q4_K_M.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q4_K_M.gguf) | Q4_K_M | 4.68GB | false | Good quality, default size for most use cases, *recommended*. |
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| [Fin-R1-Q3_K_XL.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q3_K_XL.gguf) | Q3_K_XL | 4.57GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
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| [Fin-R1-Q4_K_S.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q4_K_S.gguf) | Q4_K_S | 4.46GB | false | Slightly lower quality with more space savings, *recommended*. |
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| [Fin-R1-Q4_0.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q4_0.gguf) | Q4_0 | 4.44GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
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| [Fin-R1-IQ4_NL.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ4_NL.gguf) | IQ4_NL | 4.44GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
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| [Fin-R1-IQ4_XS.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ4_XS.gguf) | IQ4_XS | 4.22GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
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| [Fin-R1-Q3_K_L.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q3_K_L.gguf) | Q3_K_L | 4.09GB | false | Lower quality but usable, good for low RAM availability. |
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| [Fin-R1-Q3_K_M.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q3_K_M.gguf) | Q3_K_M | 3.81GB | false | Low quality. |
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| [Fin-R1-IQ3_M.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ3_M.gguf) | IQ3_M | 3.57GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
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| [Fin-R1-Q2_K_L.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q2_K_L.gguf) | Q2_K_L | 3.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
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| [Fin-R1-Q3_K_S.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q3_K_S.gguf) | Q3_K_S | 3.49GB | false | Low quality, not recommended. |
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| [Fin-R1-IQ3_XS.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ3_XS.gguf) | IQ3_XS | 3.35GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
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| [Fin-R1-IQ3_XXS.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ3_XXS.gguf) | IQ3_XXS | 3.11GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
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| [Fin-R1-Q2_K.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-Q2_K.gguf) | Q2_K | 3.02GB | false | Very low quality but surprisingly usable. |
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| [Fin-R1-IQ2_M.gguf](https://huggingface.co/bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF/blob/main/SUFE-AIFLM-Lab_Fin-R1-IQ2_M.gguf) | IQ2_M | 2.78GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
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## Embed/output weights
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
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## Downloading using huggingface-cli
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<details>
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<summary>Click to view download instructions</summary>
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First, make sure you have hugginface-cli installed:
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```
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pip install -U "huggingface_hub[cli]"
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```
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Then, you can target the specific file you want:
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```
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huggingface-cli download bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF --include "SUFE-AIFLM-Lab_Fin-R1-Q4_K_M.gguf" --local-dir ./
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```
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
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```
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huggingface-cli download bartowski/SUFE-AIFLM-Lab_Fin-R1-GGUF --include "SUFE-AIFLM-Lab_Fin-R1-Q8_0/*" --local-dir ./
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```
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You can either specify a new local-dir (SUFE-AIFLM-Lab_Fin-R1-Q8_0) or download them all in place (./)
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</details>
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## ARM/AVX information
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Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
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Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
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As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
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Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
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<details>
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<summary>Click to view Q4_0_X_X information (deprecated</summary>
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I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
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<details>
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<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
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| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
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Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
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</details>
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</details>
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## Which file should I choose?
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<details>
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<summary>Click here for details</summary>
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A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
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The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
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If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
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If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
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Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
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If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
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If you want to get more into the weeds, you can check out this extremely useful feature chart:
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[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
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But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
|
||||||
|
|
||||||
|
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## Credits
|
||||||
|
|
||||||
|
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
|
||||||
|
|
||||||
|
Thank you ZeroWw for the inspiration to experiment with embed/output.
|
||||||
|
|
||||||
|
Thank you to LM Studio for sponsoring my work.
|
||||||
|
|
||||||
|
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
||||||
3
SUFE-AIFLM-Lab_Fin-R1-IQ2_M.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-IQ2_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
||||||
|
size 2780342336
|
||||||
3
SUFE-AIFLM-Lab_Fin-R1-IQ3_M.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-IQ3_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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oid sha256:ba72629b704fdeed5565dbd808e7e6771094b72e7f49538432218042df8e5bf3
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||||||
|
size 3574011968
|
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3
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Normal file
3
SUFE-AIFLM-Lab_Fin-R1-IQ3_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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oid sha256:0dbd84f972766fc213242881e20c6fa92dbdbd9bc9ddd32c4f080d186847fa52
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|
size 3346255936
|
||||||
3
SUFE-AIFLM-Lab_Fin-R1-IQ3_XXS.gguf
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3
SUFE-AIFLM-Lab_Fin-R1-IQ3_XXS.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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oid sha256:9e97c7196285f97f54d625b9ea52e837166313f967f98f31e43e98777bd513f5
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||||||
|
size 3114514496
|
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3
SUFE-AIFLM-Lab_Fin-R1-IQ4_NL.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-IQ4_NL.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:bf4e469fe5fd03ab99722b8adaaee8917c9d84f5cb416a6095540f7711003cea
|
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|
size 4437813312
|
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3
SUFE-AIFLM-Lab_Fin-R1-IQ4_XS.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-IQ4_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:0d12ea70387c78f3bc45cac7ee9bb42c02540efe07d91109c01d16ee13a5bef7
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|
size 4218472512
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q2_K.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q2_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:1dc6b522010eef3d71133de8dd3e16a40167f09ec53e7843de7a226d3bf16856
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|
size 3015940160
|
||||||
3
SUFE-AIFLM-Lab_Fin-R1-Q2_K_L.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q2_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:9d9edc78ccc948eb3ea126f587c3b421fd773fad3b560a47df569d54f242f443
|
||||||
|
size 3548164160
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_L.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:e4a9c365c411650e751ddb2b42964183b5f8ee3730c65476e7cf0034c3cbc975
|
||||||
|
size 4088459328
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_M.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:c20b70ebec99c987ffc950db112b704f966b407685b4685b559a56998a995efe
|
||||||
|
size 3808391232
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_S.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:3595900ee8058dcb4692a149602e815c54f193c5f0deb260d107d666f82f602d
|
||||||
|
size 3492368448
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_XL.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q3_K_XL.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:cc64327f1387c442dfba695129bf7e3120abd66688b08730e7960460a044158c
|
||||||
|
size 4565332032
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q4_0.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q4_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:c4f0538a7a9335765df02b518dbc2800110705227a5a5303148fdda5cf45e384
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||||||
|
size 4444121152
|
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SUFE-AIFLM-Lab_Fin-R1-Q4_1.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q4_1.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:f33cf03e9e7cf67be942420cd5a994b67d5b668a2137234d6d758e25ad629f40
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|
size 4873283648
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3
SUFE-AIFLM-Lab_Fin-R1-Q4_K_L.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q4_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:0bf5fe7b6581a7723e1f4370e80d920a5690204ce0d9bb3b259eba8a1debe6c2
|
||||||
|
size 5087563840
|
||||||
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SUFE-AIFLM-Lab_Fin-R1-Q4_K_M.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:d50f16c5149b4dc103c68e249a136ab7c82f7569a7df707a2d6150bff5994c33
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||||||
|
size 4683073600
|
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SUFE-AIFLM-Lab_Fin-R1-Q4_K_S.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q4_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:6d01f8a5729dc573f79e4c7df44d68e3d564264f0df887881fa875e6a7c07970
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|
size 4457769024
|
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3
SUFE-AIFLM-Lab_Fin-R1-Q5_K_L.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q5_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:b3fdc73c30cc697052332d69f8c60f6754a2409746ccd8760a2c2ea448bf4d40
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|
size 5781196864
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Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q5_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:c7377236ecdda291058a6c096ac4f3b19764de70efc23a537d30782e22bdb430
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|
size 5444831296
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SUFE-AIFLM-Lab_Fin-R1-Q5_K_S.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q5_K_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:dcff7bc7289aa2450e13dd5bc2cd5c4f3a496aa408c1891e42f4f224e34dacb2
|
||||||
|
size 5315176512
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SUFE-AIFLM-Lab_Fin-R1-Q6_K.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q6_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:10a9f2315b98ef67ad8ea34750da125566f8d24c0075a5df2cb3845e6a2fa177
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||||||
|
size 6254198848
|
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SUFE-AIFLM-Lab_Fin-R1-Q6_K_L.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q6_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:f96f7d8d0b53dba92237a837c5be8654cf9a30157b6a5029417d8a7d443136d3
|
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|
size 6518181952
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SUFE-AIFLM-Lab_Fin-R1-Q8_0.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:999b13aec7a6703d2fa108cabda180427e1223dcec9e525c29a1d894863b1a66
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|
size 8098525248
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SUFE-AIFLM-Lab_Fin-R1-bf16.gguf
Normal file
3
SUFE-AIFLM-Lab_Fin-R1-bf16.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:cc82da151a956ca1612bacc93a94467e27c8f81bca32a8b1cec16d785730a764
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||||||
|
size 15237852992
|
||||||
BIN
SUFE-AIFLM-Lab_Fin-R1.imatrix
Normal file
BIN
SUFE-AIFLM-Lab_Fin-R1.imatrix
Normal file
Binary file not shown.
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
Reference in New Issue
Block a user