bartowski/LiquidAI_LFM2.5-2.6B-GGUF
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: bartowski/LiquidAI_LFM2.5-2.6B-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-08-05 16:17:13 +08:00
commit f40607d3c4
28 changed files with 298 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

47
.gitattributes vendored Normal file
View File

@@ -0,0 +1,47 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bin.* filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zstandard filter=lfs diff=lfs merge=lfs -text
*.tfevents* filter=lfs diff=lfs merge=lfs -text
*.db* filter=lfs diff=lfs merge=lfs -text
*.ark* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.gguf* filter=lfs diff=lfs merge=lfs -text
*.ggml filter=lfs diff=lfs merge=lfs -text
*.llamafile* filter=lfs diff=lfs merge=lfs -text
*.pt2 filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text

3
LiquidAI_LFM2.5-2.6B-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c4411044044c637274ad07cab250bad3dd9d804b3d5fd823827f78736ea84ce6
size 1022470432

3
LiquidAI_LFM2.5-2.6B-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3e6d4876484c09246831062afc4961efa45405e06c622765f8e5c31c484b4096
size 1292470560

3
LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6ad8fa13972d24c82527b858f47224344169048cb8481cc2bae029b2f2baae95
size 1229596960

3
LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:60e72d4976b7a3b43101a05563624d4c55cb5b8e10a1d8f5ea80194cd9b25f08
size 1130490144

3
LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e6ef7c1c666977f144c23693bdae622775940fb45264f7ae9fbc717488daf355
size 1598220576

3
LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:34844da05ab6e4ca773299d97a2691e47f4f0e1587a3a632f4ca82b3916754b6
size 1522657568

3
LiquidAI_LFM2.5-2.6B-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:67bc2f2c18cff83ffd82d3c83583f01a1ed937ba860ab7de052235bc8d2ce880
size 1102850336

3
LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:455470f8d123d034df5b8fbb2aac1774018a37256c78ff9f3622fc5392ef840d
size 1166338336

3
LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e98aec36cffe8f21c37bd1b230caa424e44746b2c4f5a31625e2973b26a7e402
size 1454008608

3
LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:08dc6cb8cfe80722e20e523564f49d2624265721f7b596d6b9c167001b058ff9
size 1373661472

3
LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b6c2408cf0d287b51268ba131d43f388fb534ad31719b53cc13df26ece5e2ed7
size 1277077792

3
LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cad506e52d105bc8138e42cac42f347c320584da9fc966cc35d98b63282512bc
size 1517496608

3
LiquidAI_LFM2.5-2.6B-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:68b0e51637960e8c75c32ef10b73ed241a2cecbf8224fd8fb720434b15dc304d
size 1602349344

3
LiquidAI_LFM2.5-2.6B-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:261c0986fb6d97463c16eb0873433ddfe244e03bda2edc0b5da95aa2436eecdf
size 1749346592

3
LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:240d0d0e2415f297772580c6480eeaab38c65f57676570367fa0300c3cfef8d3
size 1747511584

3
LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:03ab6106f4636ae7f316245d2adf7394ccf3cd80a684ec0c473b1a4720f3108e
size 1684023584

3
LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d2109c5be64fdbd0a4139d136190be1ef96068cae384beb1fedd51deb8eb40a3
size 1607002400

3
LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3fcf7008dfc678e8129b418d407a5fdce6509338c271b10ecc60790885a97a2b
size 2011031840

3
LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:89f1f9739a61bd8edf245b7b0c24cb2a20abc287665e35653a3772e7474506a6
size 1947543840

3
LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:73e692b159fea4e02423952bdca1cca5ba694c0ebad2c9293f045e96f2a8f0c3
size 1900472608

3
LiquidAI_LFM2.5-2.6B-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:499c120820935273c5eec587333ce18e0d4369911bd795b537f041ab4b20052f
size 2236852512

3
LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6ebd3b3d64967bbdbfcf0f0bbacc18686d9faab552c9d369ba18743cd68148c7
size 2300340512

3
LiquidAI_LFM2.5-2.6B-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:83bbdbb27c8a2b8636e554f364dad63f76588d4dec4629c819093a0317f07da8
size 2874779936

3
LiquidAI_LFM2.5-2.6B-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:917b46ebb876cfbda1243567a819bd754cdaa8d912508e0030938792182ef66a
size 5403158560

3
LiquidAI_LFM2.5-2.6B-imatrix.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b6dfa9f4fac87d56a418a6cb9774c849fc5d375bc08b0dfcfb3ba091c4d79fef
size 2430880

175
README.md Normal file
View File

@@ -0,0 +1,175 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
license_link: LICENSE
language:
- ar
- zh
- en
- fr
- de
- hi
- id
- it
- ja
- ko
- pl
- pt
- ru
- es
- th
- vi
base_model_relation: quantized
base_model: LiquidAI/LFM2.5-2.6B
license_name: lfm1.0
tags:
- liquid
- lfm2.5
- edge
license: other
---
## Llamacpp imatrix Quantizations of LFM2.5-2.6B by LiquidAI
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10262">b10262</a> for quantization.
Original model: https://huggingface.co/LiquidAI/LFM2.5-2.6B
**Model details:**
- Parameter count: 3B
- Input support: text
- MTP: no
- imatrix: yes - [details](#imatrix)
[How to run](#how-to-run)
## Prompt format
```
<|startoftext|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
```
**Don't know which to choose?** Grab [Q4_K_M](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf) (1.68GB) - usually a good mix of size and performance. Download instructions available [here](#downloading-using-the-hugging-face-cli)
## Available files:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [LiquidAI_LFM2.5-2.6B-bf16.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-bf16.gguf) | bf16 | 5.40GB | false | Full BF16 weights. |
| [LiquidAI_LFM2.5-2.6B-Q8_0.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q8_0.gguf) | Q8_0 | 2.87GB | false | Extremely high quality, generally unneeded but max available quant. |
| [LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf) | Q6_K_L | 2.30GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q6_K.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q6_K.gguf) | Q6_K | 2.24GB | false | Very high quality, near perfect, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf) | Q5_K_L | 2.01GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf) | Q5_K_M | 1.95GB | false | High quality, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf) | Q5_K_S | 1.90GB | false | High quality, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q4_1.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_1.gguf) | Q4_1 | 1.75GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf) | Q4_K_L | 1.75GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf) | Q4_K_M | 1.68GB | false | Good quality, default size for most use cases, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf) | Q4_K_S | 1.61GB | false | Slightly lower quality with more space savings, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q4_0.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_0.gguf) | Q4_0 | 1.60GB | false | Legacy format, kept for compatibility with older tools. |
| [LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf) | IQ4_NL | 1.60GB | false | Similar to IQ4_XS, but slightly larger. |
| [LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf) | IQ4_XS | 1.52GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf) | Q3_K_XL | 1.52GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf) | Q3_K_L | 1.45GB | false | Lower quality but usable, good for low RAM availability. |
| [LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf) | Q3_K_M | 1.37GB | false | Low quality. |
| [LiquidAI_LFM2.5-2.6B-IQ3_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_M.gguf) | IQ3_M | 1.29GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf) | Q3_K_S | 1.28GB | false | Low quality, not recommended. |
| [LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf) | IQ3_XS | 1.23GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf) | Q2_K_L | 1.17GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf) | IQ3_XXS | 1.13GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [LiquidAI_LFM2.5-2.6B-Q2_K.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q2_K.gguf) | Q2_K | 1.10GB | false | Very low quality but surprisingly usable. |
| [LiquidAI_LFM2.5-2.6B-IQ2_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ2_M.gguf) | IQ2_M | 1.02GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
Download a specific file:
```
hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./
```
## Downloading using the Hugging Face CLI
<details>
<summary>Click to view download instructions</summary>
First, make sure you have the Hugging Face CLI installed:
```
pip install -U "huggingface_hub[cli]"
```
Download a specific file:
```
hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./
```
</details>
## How to run
These quants run with [llama.cpp](https://github.com/ggml-org/llama.cpp) - installable in one line via [llama.app](https://llama.app/):
```
curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
```
llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.
These quants were made with llama.cpp release b10262 - if this model's architecture is newly supported, you'll need that release or newer to run them.
They also work in: [LM Studio](https://lmstudio.ai/) · [koboldcpp](https://github.com/LostRuins/koboldcpp) · [ramalama](https://github.com/containers/ramalama) · [Jan AI](https://www.jan.ai/) · [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui) · [LoLLMs](https://github.com/ParisNeo/lollms) · [Atomic Chat](https://atomic.chat/)
## imatrix
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d). The imatrix is available here: [LiquidAI_LFM2.5-2.6B-imatrix.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-imatrix.gguf).
## Embed/output weights
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.
## ARM/AVX information
llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in [this PR](https://github.com/ggml-org/llama.cpp/pull/9921). This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.
## Which file should I choose?
<details>
<summary>Click here for details</summary>
An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
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.
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.
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.
Hugging Face can also do this math for you: add your hardware in your [Local Apps settings](https://huggingface.co/settings/local-apps) and the model page will show which files fit.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
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.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix)
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.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

1
configuration.json Normal file
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}