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---
base_model: Salesforce/xLAM-8x22b-r
datasets:
- Salesforce/xlam-function-calling-60k
extra_gated_button_content: Agree and access repository
extra_gated_heading: Acknowledge to follow corresponding license to access the repository
inference: false
language:
- en
library_name: gguf
license: cc-by-nc-4.0
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- function-calling
- LLM Agent
- tool-use
- mistral
- pytorch
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# xLAM-8x22b-r-IMat-GGUF
_Llama.cpp imatrix quantization of Salesforce/xLAM-8x22b-r_
Original Model: [Salesforce/xLAM-8x22b-r](https://huggingface.co/Salesforce/xLAM-8x22b-r)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3649](https://github.com/ggerganov/llama.cpp/releases/tag/b3649)
IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)
- [Files](#files)
- [IMatrix](#imatrix)
- [Common Quants](#common-quants)
- [All Quants](#all-quants)
- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
- [Inference](#inference)
- [Simple chat template](#simple-chat-template)
- [Chat template with system prompt](#chat-template-with-system-prompt)
- [Llama.cpp](#llama-cpp)
- [FAQ](#faq)
- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
---
## Files
### IMatrix
Status: ✅ Available
Link: [here](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [xLAM-8x22b-r.Q8_0/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q8_0) | Q8_0 | 149.43GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q6_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q6_K) | Q6_K | 115.54GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q4_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q4_K) | Q4_K | 85.60GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q3_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q3_K) | Q3_K | 67.80GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q2_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q2_K) | Q2_K | 52.11GB | ✅ Available | 🟢 IMatrix | ✂ Yes
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [xLAM-8x22b-r.BF16/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.BF16) | BF16 | 281.27GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.FP16/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.FP16) | F16 | 281.27GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q8_0/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q8_0) | Q8_0 | 149.43GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q6_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q6_K) | Q6_K | 115.54GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q5_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q5_K) | Q5_K | 99.98GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q5_K_S/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q5_K_S) | Q5_K_S | 96.99GB | ✅ Available | ⚪ Static | ✂ Yes
| [xLAM-8x22b-r.Q4_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q4_K) | Q4_K | 85.60GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q4_K_S/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q4_K_S) | Q4_K_S | 80.49GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ4_NL/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ4_NL) | IQ4_NL | 79.79GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ4_XS/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ4_XS) | IQ4_XS | 75.49GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q3_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q3_K) | Q3_K | 67.80GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q3_K_L/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q3_K_L) | Q3_K_L | 72.59GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q3_K_S/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q3_K_S) | Q3_K_S | 61.51GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ3_M/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ3_M) | IQ3_M | 64.50GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ3_S/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ3_S) | IQ3_S | 61.51GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ3_XS/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ3_XS) | IQ3_XS | 58.24GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ3_XXS/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ3_XXS) | IQ3_XXS | 54.91GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q2_K/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q2_K) | Q2_K | 52.11GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.Q2_K_S/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.Q2_K_S) | Q2_K_S | 48.10GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ2_M/*](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/tree/main/xLAM-8x22b-r.IQ2_M) | IQ2_M | 46.72GB | ✅ Available | 🟢 IMatrix | ✂ Yes
| [xLAM-8x22b-r.IQ2_S.gguf](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/blob/main/xLAM-8x22b-r.IQ2_S.gguf) | IQ2_S | 42.60GB | ✅ Available | 🟢 IMatrix | 📦 No
| [xLAM-8x22b-r.IQ2_XS.gguf](https://huggingface.co/legraphista/xLAM-8x22b-r-IMat-GGUF/blob/main/xLAM-8x22b-r.IQ2_XS.gguf) | IQ2_XS | 42.01GB | ✅ Available | 🟢 IMatrix | 📦 No
| xLAM-8x22b-r.IQ2_XXS | IQ2_XXS | - | ⏳ Processing | 🟢 IMatrix | -
| xLAM-8x22b-r.IQ1_M | IQ1_M | - | ⏳ Processing | 🟢 IMatrix | -
| xLAM-8x22b-r.IQ1_S | IQ1_S | - | ⏳ Processing | 🟢 IMatrix | -
## Downloading using huggingface-cli
If you do not have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Download the specific file you want:
```
huggingface-cli download legraphista/xLAM-8x22b-r-IMat-GGUF --include "xLAM-8x22b-r.Q8_0.gguf" --local-dir ./
```
If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download legraphista/xLAM-8x22b-r-IMat-GGUF --include "xLAM-8x22b-r.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<s>[INST] {user_prompt}[/INST] {assistant_response}</s>[INST] {next_user_prompt}[/INST]
```
### Chat template with system prompt
```
<s>[INST] {user_prompt}[/INST] {assistant_response}</s>[INST] {system_prompt}
{next_user_prompt}[/INST]
```
### Llama.cpp
```
llama.cpp/main -m xLAM-8x22b-r.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"
```
---
## FAQ
### Why is the IMatrix not applied everywhere?
According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
### How do I merge a split GGUF?
1. Make sure you have `gguf-split` available
- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
- Download the appropriate zip for your system from the latest release
- Unzip the archive and you should be able to find `gguf-split`
2. Locate your GGUF chunks folder (ex: `xLAM-8x22b-r.Q8_0`)
3. Run `gguf-split --merge xLAM-8x22b-r.Q8_0/xLAM-8x22b-r.Q8_0-00001-of-XXXXX.gguf xLAM-8x22b-r.Q8_0.gguf`
- Make sure to point `gguf-split` to the first chunk of the split.
---
Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)! |