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xLAM-8x22b-r-IMat-GGUF

Text Generation
GGUF
PyTorch
English
function-calling
LLM Agent
tool-use
mistral
quantized
GGUF
quantization
imat
imatrix
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
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metadata
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
Original dtype: BF16 (bfloat16)
Quantized by: llama.cpp b3649
IMatrix dataset: here

Files

IMatrix

Status: ✅ Available
Link: here

Common Quants

Filename Quant type File Size Status Uses IMatrix Is Split
xLAM-8x22b-r.Q8_0/* Q8_0 149.43GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q6_K/* Q6_K 115.54GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q4_K/* Q4_K 85.60GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q3_K/* Q3_K 67.80GB ✅ Available 🟢 IMatrix ✂ Yes
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/* BF16 281.27GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.FP16/* F16 281.27GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q8_0/* Q8_0 149.43GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q6_K/* Q6_K 115.54GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q5_K/* Q5_K 99.98GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q5_K_S/* Q5_K_S 96.99GB ✅ Available ⚪ Static ✂ Yes
xLAM-8x22b-r.Q4_K/* Q4_K 85.60GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q4_K_S/* Q4_K_S 80.49GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ4_NL/* IQ4_NL 79.79GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ4_XS/* IQ4_XS 75.49GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q3_K/* Q3_K 67.80GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q3_K_L/* Q3_K_L 72.59GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q3_K_S/* Q3_K_S 61.51GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ3_M/* IQ3_M 64.50GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ3_S/* IQ3_S 61.51GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ3_XS/* IQ3_XS 58.24GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ3_XXS/* IQ3_XXS 54.91GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q2_K/* Q2_K 52.11GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.Q2_K_S/* Q2_K_S 48.10GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ2_M/* IQ2_M 46.72GB ✅ Available 🟢 IMatrix ✂ Yes
xLAM-8x22b-r.IQ2_S.gguf IQ2_S 42.60GB ✅ Available 🟢 IMatrix 📦 No
xLAM-8x22b-r.IQ2_XS.gguf IQ2_XS 42.01GB ✅ Available 🟢 IMatrix 📦 No
xLAM-8x22b-r.IQ2_XXS.gguf IQ2_XXS 37.89GB ✅ Available 🟢 IMatrix 📦 No
xLAM-8x22b-r.IQ1_M.gguf IQ1_M 32.74GB ✅ Available 🟢 IMatrix 📦 No
xLAM-8x22b-r.IQ1_S.gguf IQ1_S 29.65GB ✅ Available 🟢 IMatrix 📦 No

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, 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
  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!