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xLAM-7b-r-GGUF

Text Generation
Transformers
GGUF
PyTorch
English
function-calling
LLM Agent
tool-use
mistral
TensorBlock
GGUF
Inference Endpoints
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metadata
extra_gated_heading: Acknowledge to follow corresponding license to access the repository
extra_gated_button_content: Agree and access repository
extra_gated_fields:
  First Name: text
  Last Name: text
  Country: country
  Affiliation: text
license: cc-by-nc-4.0
datasets:
  - Salesforce/xlam-function-calling-60k
language:
  - en
pipeline_tag: text-generation
tags:
  - function-calling
  - LLM Agent
  - tool-use
  - mistral
  - pytorch
  - TensorBlock
  - GGUF
library_name: transformers
base_model: Salesforce/xLAM-7b-r
TensorBlock

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Salesforce/xLAM-7b-r - GGUF

This repo contains GGUF format model files for Salesforce/xLAM-7b-r.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template 

<s>[INST] {prompt} [/INST]

Model file specification

Filename Quant type File Size Description
xLAM-7b-r-Q2_K.gguf Q2_K 2.532 GB smallest, significant quality loss - not recommended for most purposes
xLAM-7b-r-Q3_K_S.gguf Q3_K_S 2.947 GB very small, high quality loss
xLAM-7b-r-Q3_K_M.gguf Q3_K_M 3.277 GB very small, high quality loss
xLAM-7b-r-Q3_K_L.gguf Q3_K_L 3.560 GB small, substantial quality loss
xLAM-7b-r-Q4_0.gguf Q4_0 3.827 GB legacy; small, very high quality loss - prefer using Q3_K_M
xLAM-7b-r-Q4_K_S.gguf Q4_K_S 3.856 GB small, greater quality loss
xLAM-7b-r-Q4_K_M.gguf Q4_K_M 4.068 GB medium, balanced quality - recommended
xLAM-7b-r-Q5_0.gguf Q5_0 4.654 GB legacy; medium, balanced quality - prefer using Q4_K_M
xLAM-7b-r-Q5_K_S.gguf Q5_K_S 4.654 GB large, low quality loss - recommended
xLAM-7b-r-Q5_K_M.gguf Q5_K_M 4.779 GB large, very low quality loss - recommended
xLAM-7b-r-Q6_K.gguf Q6_K 5.534 GB very large, extremely low quality loss
xLAM-7b-r-Q8_0.gguf Q8_0 7.167 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/xLAM-7b-r-GGUF --include "xLAM-7b-r-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/xLAM-7b-r-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'