Instructions to use skshmjn/llama-3.2-3B-Mongo-query-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with Transformers:

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

pipe = pipeline("text-generation", model="skshmjn/llama-3.2-3B-Mongo-query-generator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("skshmjn/llama-3.2-3B-Mongo-query-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use skshmjn/llama-3.2-3B-Mongo-query-generator 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 skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf skshmjn/llama-3.2-3B-Mongo-query-generator: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 skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf skshmjn/llama-3.2-3B-Mongo-query-generator: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 skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

Use Docker

docker model run hf.co/skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

LM Studio
Jan

vLLM

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skshmjn/llama-3.2-3B-Mongo-query-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skshmjn/llama-3.2-3B-Mongo-query-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

SGLang

How to use skshmjn/llama-3.2-3B-Mongo-query-generator 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 "skshmjn/llama-3.2-3B-Mongo-query-generator" \
    --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": "skshmjn/llama-3.2-3B-Mongo-query-generator",
		"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 "skshmjn/llama-3.2-3B-Mongo-query-generator" \
        --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": "skshmjn/llama-3.2-3B-Mongo-query-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use skshmjn/llama-3.2-3B-Mongo-query-generator with Ollama:
```
ollama run hf.co/skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
```

Unsloth Studio

How to use skshmjn/llama-3.2-3B-Mongo-query-generator 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 skshmjn/llama-3.2-3B-Mongo-query-generator 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 skshmjn/llama-3.2-3B-Mongo-query-generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for skshmjn/llama-3.2-3B-Mongo-query-generator to start chatting

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skshmjn/llama-3.2-3B-Mongo-query-generator: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": "skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

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 "skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M" \
  --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 skshmjn/llama-3.2-3B-Mongo-query-generator with Docker Model Runner:
```
docker model run hf.co/skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M
```

Lemonade

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.2-3B-Mongo-query-generator-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use skshmjn/llama-3.2-3B-Mongo-query-generator with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skshmjn/llama-3.2-3B-Mongo-query-generator: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 skshmjn/llama-3.2-3B-Mongo-query-generator:Q4_K_M

Run Hermes

hermes

Atomic Chat

MongoDB Query Generator - Llama-3.2-3B (Fine-tuned)

Developed by: skshmjn
License: apache-2.0
Finetuned from model: unsloth/Llama-3.2-3B-Instruct
Dataset Used: skshmjn/mongodb-chat-query
Supports: Transformers & GGUF (for fast inference on CPU/GPU)

🚀 Model Overview

This model is designed to generate MongoDB queries from natural language prompts. It supports:

Basic CRUD operations: find, insert, update, delete
Aggregation Pipelines: $group, $match, $lookup, $sort, etc.
Indexing & Performance Queries
Nested Queries & Joins ($lookup)

Trained using Unsloth for efficient fine-tuning and GGUF quantization for fast inference.

📌 Example Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "skshmjn/Llama-3.2-3B-Mongo-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
schema = {} # Pass your mongodb schema here, leave empty for generic queries. Sample available in hugging face's repository

prompt = "Here is mongodb schema {schema} and Find all employees older than 30 in the 'employees' collection."
inputs = tokenizer(prompt, return_tensors="pt")

output = model.generate(**inputs, max_length=100)
query = tokenizer.decode(output[0], skip_special_tokens=True)

print(query)

Downloads last month: 145

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for skshmjn/llama-3.2-3B-Mongo-query-generator

Base model

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

Finetuned

unsloth/Llama-3.2-3B-Instruct

Quantized

(112)

this model

skshmjn
/

llama-3.2-3B-Mongo-query-generator

MongoDB Query Generator - Llama-3.2-3B (Fine-tuned)

🚀 Model Overview

📌 Example Usage (Transformers)

Model tree for skshmjn/llama-3.2-3B-Mongo-query-generator

Dataset used to train skshmjn/llama-3.2-3B-Mongo-query-generator