---
base_model: Salesforce/xLAM-7b-r
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- function-calling
- LLM Agent
- tool-use
- mistral
- pytorch
- llama-cpp
- gguf-my-repo
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
---

# Solshine/xLAM-7b-r-Q2_K-GGUF
This model was converted to GGUF format from [`Salesforce/xLAM-7b-r`](https://huggingface.co/Salesforce/xLAM-7b-r) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Salesforce/xLAM-7b-r) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Solshine/xLAM-7b-r-Q2_K-GGUF --hf-file xlam-7b-r-q2_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Solshine/xLAM-7b-r-Q2_K-GGUF --hf-file xlam-7b-r-q2_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Solshine/xLAM-7b-r-Q2_K-GGUF --hf-file xlam-7b-r-q2_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Solshine/xLAM-7b-r-Q2_K-GGUF --hf-file xlam-7b-r-q2_k.gguf -c 2048
```