Instructions to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF",
	filename=" llama-3.2-3b-it-grpo-250404.BF16.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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/llama-3.2-3b-it-grpo-250404-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": "prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-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 "prithivMLmods/llama-3.2-3b-it-grpo-250404-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": "prithivMLmods/llama-3.2-3b-it-grpo-250404-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 "prithivMLmods/llama-3.2-3b-it-grpo-250404-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": "prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF to start chatting

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/llama-3.2-3b-it-grpo-250404-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": "prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-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 prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.2-3b-it-grpo-250404-GGUF-Q4_K_M

List all available models

lemonade list

llama-3.2-3b-it-grpo-250404-GGUF

ReZero-v0.1-llama-3.2-3b-it-grpo-250404 is a research project focused on enhancing the search abilities of small language models by training them to develop robust search strategies rather than memorizing static data. The model, built on a Llama-3.2-3B backbone, interacts with multiple synthetic search engines that each have unique retrieval mechanisms, enabling it to refine queries iteratively and persist in finding exact answers using reinforcement learning. The repository provides setup instructions, including environment configuration and dependency installation, as well as scripts to train the model or regenerate synthetic training data. Demonstrations can be run through a Gradio interface, and the release includes comprehensive experiment logs on reward strategies and search quality. The model and associated resources are open-source and accessible to the research community, with further details on experiments and references provided in the documentation.

Model Files

File name	Size	Quant Type
llama-3.2-3b-it-grpo-250404.F32.gguf	12.9 GB	F32
llama-3.2-3b-it-grpo-250404.BF16.gguf	6.43 GB	BF16
llama-3.2-3b-it-grpo-250404.F16.gguf	6.43 GB	F16
llama-3.2-3b-it-grpo-250404.Q8_0.gguf	3.42 GB	Q8_0
llama-3.2-3b-it-grpo-250404.Q6_K.gguf	2.64 GB	Q6_K
llama-3.2-3b-it-grpo-250404.Q5_K_M.gguf	2.32 GB	Q5_K_M
llama-3.2-3b-it-grpo-250404.Q5_K_S.gguf	2.27 GB	Q5_K_S
llama-3.2-3b-it-grpo-250404.Q4_K_M.gguf	2.02 GB	Q4_K_M
llama-3.2-3b-it-grpo-250404.Q4_K_S.gguf	1.93 GB	Q4_K_S
llama-3.2-3b-it-grpo-250404.Q3_K_L.gguf	1.82 GB	Q3_K_L
llama-3.2-3b-it-grpo-250404.Q3_K_M.gguf	1.69 GB	Q3_K_M
llama-3.2-3b-it-grpo-250404.Q3_K_S.gguf	1.54 GB	Q3_K_S
llama-3.2-3b-it-grpo-250404.Q2_K.gguf	1.36 GB	Q2_K

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 18

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/llama-3.2-3b-it-grpo-250404-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

Menlo/ReZero-v0.1-llama-3.2-3b-it-grpo-250404

Quantized

(7)

this model