Instructions to use artificialguybr/QWEN-2.5-0.5B-Synthia-I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artificialguybr/QWEN-2.5-0.5B-Synthia-I with Transformers:

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

pipe = pipeline("text-generation", model="artificialguybr/QWEN-2.5-0.5B-Synthia-I")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("artificialguybr/QWEN-2.5-0.5B-Synthia-I")
model = AutoModelForCausalLM.from_pretrained("artificialguybr/QWEN-2.5-0.5B-Synthia-I")
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

vLLM

How to use artificialguybr/QWEN-2.5-0.5B-Synthia-I with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artificialguybr/QWEN-2.5-0.5B-Synthia-I"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/QWEN-2.5-0.5B-Synthia-I",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artificialguybr/QWEN-2.5-0.5B-Synthia-I

SGLang

How to use artificialguybr/QWEN-2.5-0.5B-Synthia-I 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/QWEN-2.5-0.5B-Synthia-I" \
    --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": "artificialguybr/QWEN-2.5-0.5B-Synthia-I",
		"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 "artificialguybr/QWEN-2.5-0.5B-Synthia-I" \
        --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": "artificialguybr/QWEN-2.5-0.5B-Synthia-I",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use artificialguybr/QWEN-2.5-0.5B-Synthia-I with Docker Model Runner:
```
docker model run hf.co/artificialguybr/QWEN-2.5-0.5B-Synthia-I
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-0.5B Fine-tuned on Synthia v1.5-I

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the Synthia v1.5-I dataset, which contains over 20.7k instruction-following examples.

Model Description

Qwen2.5-0.5B is part of the latest Qwen2.5 series of large language models. The base model brings significant improvements in:

Instruction following and generating long texts
Understanding structured data and generating structured outputs
Support for over 29 languages
Long context support up to 32,768 tokens

This fine-tuned version enhances the base model's instruction-following capabilities through training on the Synthia v1.5-I dataset.

Model Architecture

Type: Causal Language Model
Parameters: 0.49B (0.36B non-embedding)
Layers: 24
Attention Heads: 14 for Q and 2 for KV (GQA)
Context Length: 32,768 tokens
Training Framework: Transformers 4.45.0.dev0

Intended Uses & Limitations

This model is intended for:

Instruction following and task completion
Text generation and completion
Conversational AI applications

The model inherits the multilingual capabilities and long context support of the base Qwen2.5-0.5B model, while being specifically tuned for instruction following.

Training Procedure

Training Data

The model was fine-tuned on the Synthia v1.5-I dataset containing 20.7k instruction-following examples.

Training Hyperparameters

The following hyperparameters were used during training:

Learning rate: 1e-05
Train batch size: 5
Eval batch size: 5
Seed: 42
Gradient accumulation steps: 8
Total train batch size: 40
Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
LR scheduler type: cosine
LR scheduler warmup steps: 100
Number of epochs: 3
Sequence length: 4096
Sample packing: enabled

🌐 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
Pad to sequence length: enabled

Framework Versions

Transformers 4.45.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1

See axolotl config

axolotl version: 0.4.1

Downloads last month: 1,772

Model tree for artificialguybr/QWEN-2.5-0.5B-Synthia-I

Base model

Qwen/Qwen2.5-0.5B

Finetuned

(628)

this model

Quantizations

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