Instructions to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF", device_map="auto")

llama-cpp-python

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF",
	filename="Llama-xLAM-2-8b-fc-r-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

SGLang

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF to start chatting

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF:Q2_K

Run and chat with the model

lemonade run user.Salesforce_Llama-xLAM-2-8b-fc-r-GGUF-Q2_K

List all available models

lemonade list

morriszms commited on Apr 26, 2025

Commit

cefd7b9

verified ·

1 Parent(s): bf0a53f

Upload folder using huggingface_hub

Browse files

Files changed (14) hide show

.gitattributes +12 -0
Llama-xLAM-2-8b-fc-r-Q2_K.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q3_K_L.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q3_K_M.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q3_K_S.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q4_0.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q4_K_M.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q4_K_S.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q5_0.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q5_K_M.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q5_K_S.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q6_K.gguf +3 -0
Llama-xLAM-2-8b-fc-r-Q8_0.gguf +3 -0
README.md +130 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-xLAM-2-8b-fc-r-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

Llama-xLAM-2-8b-fc-r-Q2_K.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:533428cf9040963584e83a06663b001864a43cbd4663fd4500904adb43955bcb
+size 3179135200

Llama-xLAM-2-8b-fc-r-Q3_K_L.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:ba4da32908624d522d0f584002dbd4d48c750222f29e4987ab18529d44c51f4b
+size 4321960160

Llama-xLAM-2-8b-fc-r-Q3_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:98ad47f42815166356ea1078a5d7b3ced4a3a16d19a145c4427963786430baa3
+size 4018921696

Llama-xLAM-2-8b-fc-r-Q3_K_S.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:84b01c15d9fef2915507894d44d334a2f9cede38942a96770f66c22d7be7d312
+size 3664503008

Llama-xLAM-2-8b-fc-r-Q4_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:6957333675a57f07616a38b71cb481c136ac1cf9d8b94592ba104bf4584d9a74
+size 4661215456

Llama-xLAM-2-8b-fc-r-Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f14ce502402784c827af6c9f8267474e84ecdf64a55f9ca1c0c2772c6845be4b
+size 4920738016

Llama-xLAM-2-8b-fc-r-Q4_K_S.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:c1ea942c758e3e02ad031075554502624d31a0d547f83799824bc8bf2223035c
+size 4692672736

Llama-xLAM-2-8b-fc-r-Q5_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:2d5c8c122b7e7eae67db39f2b9e7e9f838fd063441d618596ff3611b7486c111
+size 5599297760

Llama-xLAM-2-8b-fc-r-Q5_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:06bc4d6fc09207f52a6ce4c892b74b4fb2b9992c7a7ea7cd20e25740ba20cf1c
+size 5732991200

Llama-xLAM-2-8b-fc-r-Q5_K_S.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:81bb233f71a15051e09083a6e2c71630c5a05ac8b38da240f96602045e681f03
+size 5599297760

Llama-xLAM-2-8b-fc-r-Q6_K.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:89a8c3f387fcf79b23868e409553e1602ff82c9fc3f68c5cdcc0697177e4aacb
+size 6596010208

Llama-xLAM-2-8b-fc-r-Q8_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:ac1b5959c0c8f10013160ce1c2a93fde3bd9c38d4525494dd446fbfa0a027d55
+size 8540774624

README.md ADDED Viewed

	@@ -0,0 +1,130 @@

+---
+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
+- llama
+- qwen
+- pytorch
+- LLaMA-factory
+- TensorBlock
+- GGUF
+library_name: transformers
+base_model: Salesforce/Llama-xLAM-2-8b-fc-r
+---
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;">
+            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
+        </p>
+    </div>
+</div>
+## Salesforce/Llama-xLAM-2-8b-fc-r - GGUF
+This repo contains GGUF format model files for [Salesforce/Llama-xLAM-2-8b-fc-r](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r).
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).
+## Our projects
+<table border="1" cellspacing="0" cellpadding="10">
+<tr>
+  <th style="font-size: 25px;">Awesome MCP Servers</th>
+  <th style="font-size: 25px;">TensorBlock Studio</th>
+</tr>
+  <tr>
+    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
+    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
+  </tr>
+  <tr>
+    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
+    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
+  </tr>
+<tr>
+  <th>
+    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
+      display: inline-block;
+      padding: 8px 16px;
+      background-color: #FF7F50;
+      color: white;
+      text-decoration: none;
+      border-radius: 6px;
+      font-weight: bold;
+      font-family: sans-serif;
+    ">👀 See what we built 👀</a>
+  </th>
+  <th>
+    <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
+      display: inline-block;
+      padding: 8px 16px;
+      background-color: #FF7F50;
+      color: white;
+      text-decoration: none;
+      border-radius: 6px;
+      font-weight: bold;
+      font-family: sans-serif;
+    ">👀 See what we built 👀</a>
+  </th>
+</tr>
+</table>
+## Prompt template
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+{system_prompt}
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+```
+## Model file specification
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [Llama-xLAM-2-8b-fc-r-Q2_K.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
+| [Llama-xLAM-2-8b-fc-r-Q3_K_S.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
+| [Llama-xLAM-2-8b-fc-r-Q3_K_M.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
+| [Llama-xLAM-2-8b-fc-r-Q3_K_L.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
+| [Llama-xLAM-2-8b-fc-r-Q4_0.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Llama-xLAM-2-8b-fc-r-Q4_K_S.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
+| [Llama-xLAM-2-8b-fc-r-Q4_K_M.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
+| [Llama-xLAM-2-8b-fc-r-Q5_0.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Llama-xLAM-2-8b-fc-r-Q5_K_S.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
+| [Llama-xLAM-2-8b-fc-r-Q5_K_M.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
+| [Llama-xLAM-2-8b-fc-r-Q6_K.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
+| [Llama-xLAM-2-8b-fc-r-Q8_0.gguf](https://huggingface.co/tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF/blob/main/Llama-xLAM-2-8b-fc-r-Q8_0.gguf) | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
+## Downloading instruction
+### Command line
+Firstly, install Huggingface Client
+```shell
+pip install -U "huggingface_hub[cli]"
+```
+Then, downoad the individual model file the a local directory
+```shell
+huggingface-cli download tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF --include "Llama-xLAM-2-8b-fc-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:
+```shell
+huggingface-cli download tensorblock/Salesforce_Llama-xLAM-2-8b-fc-r-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```