Instructions to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "Dinesh2001/Llama3.2-1B-QLoRA-Explainer")

Transformers

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Transformers:

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

pipe = pipeline("text-generation", model="Dinesh2001/Llama3.2-1B-QLoRA-Explainer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dinesh2001/Llama3.2-1B-QLoRA-Explainer", dtype="auto")

llama-cpp-python

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Dinesh2001/Llama3.2-1B-QLoRA-Explainer",
	filename="llama32-1b-merged-Q4_K_M.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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer 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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

Use Docker

docker model run hf.co/Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

LM Studio
Jan

vLLM

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dinesh2001/Llama3.2-1B-QLoRA-Explainer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dinesh2001/Llama3.2-1B-QLoRA-Explainer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

SGLang

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer 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 "Dinesh2001/Llama3.2-1B-QLoRA-Explainer" \
    --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": "Dinesh2001/Llama3.2-1B-QLoRA-Explainer",
		"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 "Dinesh2001/Llama3.2-1B-QLoRA-Explainer" \
        --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": "Dinesh2001/Llama3.2-1B-QLoRA-Explainer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Ollama:
```
ollama run hf.co/Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
```

Unsloth Studio

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer 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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer 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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dinesh2001/Llama3.2-1B-QLoRA-Explainer to start chatting

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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": "Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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 "Dinesh2001/Llama3.2-1B-QLoRA-Explainer: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 Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Docker Model Runner:
```
docker model run hf.co/Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M
```

Lemonade

How to use Dinesh2001/Llama3.2-1B-QLoRA-Explainer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dinesh2001/Llama3.2-1B-QLoRA-Explainer:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.2-1B-QLoRA-Explainer-Q4_K_M

List all available models

lemonade list

Llama3.2-1B-QLoRA-Explainer

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0579

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0652	0.3556	200	0.0650
0.0626	0.7111	400	0.0615
0.06	1.0658	600	0.0596
0.0596	1.4213	800	0.0591
0.0588	1.7769	1000	0.0587
0.0582	2.1316	1200	0.0584
0.0581	2.4871	1400	0.0583
0.0576	2.8427	1600	0.0579

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 3

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for Dinesh2001/Llama3.2-1B-QLoRA-Explainer

Base model

meta-llama/Llama-3.2-1B-Instruct

Adapter

(643)

this model