Instructions to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned with Transformers:

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

pipe = pipeline("text-generation", model="ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
model = AutoModelForCausalLM.from_pretrained("ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ai-nexuz/llama-3.2-1b-instruct-fine-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai-nexuz/llama-3.2-1b-instruct-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ai-nexuz/llama-3.2-1b-instruct-fine-tuned

SGLang

How to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned 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 "ai-nexuz/llama-3.2-1b-instruct-fine-tuned" \
    --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": "ai-nexuz/llama-3.2-1b-instruct-fine-tuned",
		"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 "ai-nexuz/llama-3.2-1b-instruct-fine-tuned" \
        --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": "ai-nexuz/llama-3.2-1b-instruct-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned 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 ai-nexuz/llama-3.2-1b-instruct-fine-tuned 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 ai-nexuz/llama-3.2-1b-instruct-fine-tuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ai-nexuz/llama-3.2-1b-instruct-fine-tuned to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ai-nexuz/llama-3.2-1b-instruct-fine-tuned",
    max_seq_length=2048,
)

Docker Model Runner
How to use ai-nexuz/llama-3.2-1b-instruct-fine-tuned with Docker Model Runner:
```
docker model run hf.co/ai-nexuz/llama-3.2-1b-instruct-fine-tuned
```

LLaMA-3.2-1B-Instruct Fine-Tuned Model

Model Card for Hugging Face Repository

Model Summary

This is a fine-tuned version of the LLaMA-3.2-1B-Instruct model. Fine-tuned using the kanhatakeyama/wizardlm8x22b-logical-math-coding-sft dataset, this model is specialized in logical reasoning, mathematical problem-solving, and coding tasks. Training was performed using Unsloth on Google Colab, optimized for performance and usability.

Model Details

Model Name: LLaMA-3.2-1B-Instruct (Fine-tuned)
Base Model: LLaMA-3.2-1B-Instruct
Fine-Tuning Dataset: kanhatakeyama/wizardlm8x22b-logical-math-coding-sft
Fine-Tuning Framework: Unsloth
Parameters: 1 Billion
Domain: Logical Reasoning, Mathematics, Coding
Tags: llama, fine-tuning, instruction-following, math, coding, logical-reasoning, unsloth

Fine-Tuning Dataset

The fine-tuning dataset, kanhatakeyama/wizardlm8x22b-logical-math-coding-sft, is curated for advanced reasoning tasks. It contains:

Logical reasoning scenarios
Step-by-step mathematical solutions
Complex code generation and debugging examples

Dataset Link: kanhatakeyama/wizardlm8x22b-logical-math-coding-sft

Intended Use

This model is ideal for tasks such as:

Logical Problem Solving: Derive conclusions and explanations for logical questions.
Mathematics: Solve algebra, calculus, and other mathematical problems.
Coding: Generate, debug, and explain programming code in various languages.
Instruction-Following: Handle user queries with clear and concise answers.

Example Applications:

AI tutors
Logical reasoning assistants
Math-solving bots
Code generation and debugging tools

Usage

Installation

To use this model, install the required dependencies:

pip install transformers datasets torch accelerate

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_name = "ai-nexuz/llama-3.2-1b-instruct-fine-tuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Generating Outputs

prompt = "Solve this equation: 2x + 3 = 7. Find x."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Model Training

Hardware

Platform: Google Colab Pro
GPU: NVIDIA Tesla T4

Training Configuration

Batch Size: 32
Epochs: 1

Frameworks Used

Unsloth: For efficient training
Hugging Face Transformers: For model and tokenizer handling

Limitations

While this model is highly proficient in logical reasoning, mathematics, and coding tasks, there are some limitations:

May produce inaccurate results for ambiguous or poorly-defined prompts.
Performance may degrade for highly specialized or niche coding languages.

Deployment

Using Gradio for Web UI

pip install gradio

import gradio as gr

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=200)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

gr.Interface(fn=generate_response, inputs="text", outputs="text").launch()

Hugging Face Inference API

This model can also be accessed using the Hugging Face Inference API for hosted deployment:

from transformers import pipeline

pipe = pipeline("text-generation", model="ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
result = pipe("Explain the concept of recursion in programming.")
print(result)

Acknowledgements

This fine-tuning work was made possible by:

Hugging Face for their exceptional library and dataset hosting.
Unsloth for providing an efficient fine-tuning framework.
Google Colab for GPU resources.

Citation

If you use this model in your research or project, please cite it as:

@model{llama31b_instruct_finetuned,
  title={Fine-Tuned LLaMA-3.2-1B-Instruct},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/your-huggingface-repo/llama-3.2-1b-instruct-finetuned},
}

Licensing

This model is released under the Apache 2.0 License. See LICENSE for details.

Tags:
llama fine-tuning math coding logical-reasoning instruction-following transformers

Summary:
A fine-tuned version of LLaMA-3.2-1B-Instruct specializing in logical reasoning, math problem-solving, and code generation. Perfect for AI-driven tutoring, programming assistance, and logical problem-solving tasks.

Downloads last month: 552

Safetensors

Model size

1B params

Tensor type

F16

Model tree for ai-nexuz/llama-3.2-1b-instruct-fine-tuned

Base model

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

Finetuned

unsloth/Llama-3.2-1B-Instruct