---

license: llama3.2

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

library_name: transformers

pipeline_tag: text-generation

tags:

- llama

- llama-3.2

- code

- coding-assistant

- instruct

- gguf

- unsloth

- transformers

- ollama

language:

- en

---

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

!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.