Instructions to use second-state/Llama-3.2-1B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/Llama-3.2-1B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/Llama-3.2-1B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/Llama-3.2-1B-Instruct-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("second-state/Llama-3.2-1B-Instruct-GGUF")

llama-cpp-python

How to use second-state/Llama-3.2-1B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/Llama-3.2-1B-Instruct-GGUF",
	filename="Llama-3.2-1B-Instruct-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 second-state/Llama-3.2-1B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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 second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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 second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Llama-3.2-1B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Llama-3.2-1B-Instruct-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": "second-state/Llama-3.2-1B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

SGLang

How to use second-state/Llama-3.2-1B-Instruct-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 "second-state/Llama-3.2-1B-Instruct-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": "second-state/Llama-3.2-1B-Instruct-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 "second-state/Llama-3.2-1B-Instruct-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": "second-state/Llama-3.2-1B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/Llama-3.2-1B-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/Llama-3.2-1B-Instruct-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 second-state/Llama-3.2-1B-Instruct-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 second-state/Llama-3.2-1B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/Llama-3.2-1B-Instruct-GGUF to start chatting

How to use second-state/Llama-3.2-1B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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": "second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/Llama-3.2-1B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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 second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use second-state/Llama-3.2-1B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Llama-3.2-1B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

apepkuss79 commited on Feb 8, 2025

Commit

ffae973

verified ·

1 Parent(s): 4d9e86c

Update README.md

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README.md +99 -99

README.md CHANGED Viewed

@@ -1,99 +1,99 @@
----
-base_model: meta-llama/Llama-3.2-1B-Instruct
-license: llama3.2
-model_creator: meta
-model_name: Llama-3.2-1B-Instruct
-quantized_by: Second State Inc.
-language:
-- en
-- de
-- fr
-- it
-- pt
-- hi
-- es
-- th
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- chat
-- llama
-- llama-3
----
-<!-- header start -->
-<!-- 200823 -->
-<div style="width: auto; margin-left: auto; margin-right: auto">
-<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
-</div>
-<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
-<!-- header end -->
-# Llama-3.2-1B-Instruct-GGUF
-## Original Model
-[meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
-## Run with LlamaEdge
-- LlamaEdge version: [v0.14.5](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.14.5) and above
-- Prompt template
-  - Prompt type: `llama-3-chat`
-  - Prompt string
-    ```text
-    <|begin_of_text|><|start_header_id|>system<|end_header_id|>
-    {{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
-    {{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
-    {{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
-    {{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
-    ```
-- Context size: `128000`
-- Run as LlamaEdge service
-  ```bash
-  wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-1B-Instruct-Q5_K_M.gguf \
-      llama-api-server.wasm \
-      --prompt-template llama-3-chat \
-      --ctx-size 128000 \
-      --model-name Llama-3.2-1b
-  ```
-- Run as LlamaEdge command app
-  ```bash
-  wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-1B-Instruct-Q5_K_M.gguf \
-    llama-chat.wasm \
-    --prompt-template llama-3-chat \
-    --ctx-size 128000
-  ```
-## Quantized GGUF Models
-| Name | Quant method | Bits | Size | Use case |
-| ---- | ---- | ---- | ---- | ----- |
-| [Llama-3.2-1B-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q2_K.gguf)     | Q2_K   | 2 | 581 MB| smallest, significant quality loss - not recommended for most purposes |
-| [Llama-3.2-1B-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 733 MB| small, substantial quality loss |
-| [Llama-3.2-1B-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 691 MB| very small, high quality loss |
-| [Llama-3.2-1B-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 642 MB| very small, high quality loss |
-| [Llama-3.2-1B-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_0.gguf)     | Q4_0   | 4 | 771 MB| legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Llama-3.2-1B-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 808 MB| medium, balanced quality - recommended |
-| [Llama-3.2-1B-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 776 MB| small, greater quality loss |
-| [Llama-3.2-1B-Instruct-Q5_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_0.gguf)     | Q5_0   | 5 | 893 MB| legacy; medium, balanced quality - prefer using Q4_K_M |
-| [Llama-3.2-1B-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 912 MB| large, very low quality loss - recommended |
-| [Llama-3.2-1B-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 893 MB| large, low quality loss - recommended |
-| [Llama-3.2-1B-Instruct-Q6_K.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q6_K.gguf)     | Q6_K   | 6 | 1.02 GB| very large, extremely low quality loss |
-| [Llama-3.2-1B-Instruct-Q8_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q8_0.gguf)     | Q8_0   | 8 | 1.32 GB| very large, extremely low quality loss - not recommended |
-| [Llama-3.2-1B-Instruct-f16.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-f16.gguf)       | f16    | 16 | 2.48 GB| |
-*Quantized with llama.cpp b3807*

+---
+base_model: meta-llama/Llama-3.2-1B-Instruct
+license: llama3.2
+model_creator: meta
+model_name: Llama-3.2-1B-Instruct
+quantized_by: Second State Inc.
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- chat
+- llama
+- llama-3
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+# Llama-3.2-1B-Instruct-GGUF
+## Original Model
+[meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+## Run with LlamaEdge
+- LlamaEdge version: [v0.16.5](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.16.5) and above
+- Prompt template
+  - Prompt type: `llama-3-chat`
+  - Prompt string
+    ```text
+    <|begin_of_text|><|start_header_id|>system<|end_header_id|>
+    {{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+    {{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+    {{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+    {{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+    ```
+- Context size: `128000`
+- Run as LlamaEdge service
+  ```bash
+  wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-1B-Instruct-Q5_K_M.gguf \
+      llama-api-server.wasm \
+      --prompt-template llama-3-chat \
+      --ctx-size 128000 \
+      --model-name Llama-3.2-1b
+  ```
+- Run as LlamaEdge command app
+  ```bash
+  wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-1B-Instruct-Q5_K_M.gguf \
+    llama-chat.wasm \
+    --prompt-template llama-3-chat \
+    --ctx-size 128000
+  ```
+## Quantized GGUF Models
+| Name | Quant method | Bits | Size | Use case |
+| ---- | ---- | ---- | ---- | ----- |
+| [Llama-3.2-1B-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q2_K.gguf)     | Q2_K   | 2 | 581 MB| smallest, significant quality loss - not recommended for most purposes |
+| [Llama-3.2-1B-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 733 MB| small, substantial quality loss |
+| [Llama-3.2-1B-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 691 MB| very small, high quality loss |
+| [Llama-3.2-1B-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 642 MB| very small, high quality loss |
+| [Llama-3.2-1B-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_0.gguf)     | Q4_0   | 4 | 771 MB| legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Llama-3.2-1B-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 808 MB| medium, balanced quality - recommended |
+| [Llama-3.2-1B-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 776 MB| small, greater quality loss |
+| [Llama-3.2-1B-Instruct-Q5_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_0.gguf)     | Q5_0   | 5 | 893 MB| legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Llama-3.2-1B-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 912 MB| large, very low quality loss - recommended |
+| [Llama-3.2-1B-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 893 MB| large, low quality loss - recommended |
+| [Llama-3.2-1B-Instruct-Q6_K.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q6_K.gguf)     | Q6_K   | 6 | 1.02 GB| very large, extremely low quality loss |
+| [Llama-3.2-1B-Instruct-Q8_0.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-Q8_0.gguf)     | Q8_0   | 8 | 1.32 GB| very large, extremely low quality loss - not recommended |
+| [Llama-3.2-1B-Instruct-f16.gguf](https://huggingface.co/second-state/Llama-3.2-1B-Instruct-GGUF/blob/main/Llama-3.2-1B-Instruct-f16.gguf)       | f16    | 16 | 2.48 GB| |
+*Quantized with llama.cpp b4466*