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

Libraries

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF",
	filename="llama-3.2-1b-instruct.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 ciphermosaic/Llama-3.2-1B-code-Instruct-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 ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Unsloth Studio

How to use ciphermosaic/Llama-3.2-1B-code-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 ciphermosaic/Llama-3.2-1B-code-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 ciphermosaic/Llama-3.2-1B-code-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 ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF to start chatting

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ciphermosaic/Llama-3.2-1B-code-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": "ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-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 ciphermosaic/Llama-3.2-1B-code-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 ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF: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 "ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF: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 ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Llama-3.2-1B-Code-Instruct-GGUF

A lightweight coding assistant fine tuned from Meta Llama 3.2 1B Instruct on the CodeAlpaca-20K dataset. The model is optimized for instruction following in programming related tasks such as code generation, debugging, explaining code, and answering software development questions.

The model was fine tuned using Unsloth for efficient training and merged into a standalone checkpoint before being converted to GGUF for local inference with Ollama and llama.cpp compatible runtimes.

Model Details

Property	Value
Model	Llama-3.2-1B-Code-Instruct-GGUF
Author	ciphermosaic
Base Model	Meta Llama-3.2-1B-Instruct
Fine Tuning Framework	Unsloth
Dataset	sahil2801/codeAlpaca-20k
Quantization	GGUF Q4_K_M
Intended Use	Coding Assistant

Training

The model was instruction tuned on the complete CodeAlpaca-20K dataset.

Training Configuration

Framework: Unsloth
Precision: FP16
4-bit Loading: Enabled
Per Device Batch Size: 2
Gradient Accumulation Steps: 4
Learning Rate: 2e-5
Logging Steps: 25
Save Strategy: Every Epoch

Dataset

Training was performed using:

Dataset: sahil2801/codeAlpaca-20k

The dataset contains instruction and response pairs covering various programming tasks including:

Code generation
Code explanation
Debugging
Algorithm implementation
Programming concepts
Multiple programming languages

Capabilities

The model performs well on tasks such as:

Writing Python, C++, Java, JavaScript, and other programming languages
Explaining existing code
Debugging common programming errors
Implementing algorithms and data structures
Generating functions from natural language instructions
Answering programming related questions

Prompt Format

The model follows the Llama instruction format.

Example

### Instruction:
Write a Python function to check if a string is a palindrome.

### Response:

Running with Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Write a Python function to reverse a linked list."

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=256)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Running with Ollama

After downloading the GGUF model, create a Modelfile:

FROM ./Llama-3.2-1B-Code-Instruct-Q4_K_M.gguf

Create the model:

ollama create llama32-code -f Modelfile

Run:

ollama run llama32-code

Ollama Demo

GGUF

This repository includes a GGUF version using:

Q4_K_M

Compatible with:

Ollama
llama.cpp
LM Studio
Jan
Open WebUI

Limitations

Designed primarily for coding related tasks.
May generate incorrect or non optimal solutions for complex programming problems.
Responses should be reviewed before use in production environments.
Performance depends on prompt quality and task complexity.

Intended Use

This model is intended for:

Learning programming
Code generation
Debugging
Software development assistance
Educational use
Local AI coding assistants

It is not intended for safety critical or production systems without human verification.

Acknowledgements

Meta AI for the Llama 3.2 base model.
Unsloth for efficient fine tuning.
Hugging Face for model hosting and ecosystem.
sahil2801 for the CodeAlpaca-20K dataset.

License

This model is derived from Meta Llama 3.2 and is distributed under the Llama 3.2 Community License. Please ensure compliance with the original license terms when using or redistributing this model.

Downloads last month: 238

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for ciphermosaic/Llama-3.2-1B-code-Instruct-GGUF

Base model

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

Quantized

(401)

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