Instructions to use artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond with Transformers:

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

pipe = pipeline("text-generation", model="artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond")
model = AutoModelForCausalLM.from_pretrained("artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond

SGLang

How to use artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond 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 "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond with Docker Model Runner:
```
docker model run hf.co/artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama 3.2 1B - Synthia-v1.5-II - Redmond - Fine-tuned Model

🌐 Website

You can find more of my models, projects, and information on my official website:

artificialguy.com

🚀 Prompt Hub

Need high-quality prompts for image models and LLMs? Explore findgoodprompt.com.

💖 Support My Work

If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.

Patreon: Support on Patreon
Ko-fi: Buy me a Ko-fi
Buy Me a Coffee: Buy me a Coffee This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the Synthia-v1.5-II dataset.

Thanks RedmondAI for all the GPU Support!

Model Description

The base model is Llama 3.2 1B, a multilingual large language model developed by Meta. This version has been fine-tuned on the Synthia-v1.5-II instruction dataset to improve its instruction-following capabilities.

Training Data

The model was fine-tuned on Synthia-v1.5-II.

Training Procedure

The model was trained with the following hyperparameters:

Learning rate: 2e-05
Train batch size: 1
Eval batch size: 1
Seed: 42
Gradient accumulation steps: 8
Total train batch size: 8
Optimizer: Paged AdamW 8bit (betas=(0.9,0.999), epsilon=1e-08)
LR scheduler: Cosine with 100 warmup steps
Number of epochs: 3

Framework Versions

Transformers 4.46.1
Pytorch 2.3.1+cu121
Datasets 3.0.1
Tokenizers 0.20.3

Intended Use

This model is intended for:

Instruction following tasks
Conversational AI applications
Research and development in natural language processing

Training Infrastructure

The model was trained using the Axolotl framework version 0.5.0.

License

This model is subject to the Llama 3.2 Community License Agreement. Users must comply with all terms and conditions specified in the license.

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Safetensors

Model size

1B params

Tensor type

BF16

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Merges

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Quantizations

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