Instructions to use JamesAHowieson/SoftwareArchitecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JamesAHowieson/SoftwareArchitecture with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "JamesAHowieson/SoftwareArchitecture")

Transformers

How to use JamesAHowieson/SoftwareArchitecture with Transformers:

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

pipe = pipeline("text-generation", model="JamesAHowieson/SoftwareArchitecture")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JamesAHowieson/SoftwareArchitecture")
model = AutoModelForCausalLM.from_pretrained("JamesAHowieson/SoftwareArchitecture", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JamesAHowieson/SoftwareArchitecture with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JamesAHowieson/SoftwareArchitecture

SGLang

How to use JamesAHowieson/SoftwareArchitecture 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 "JamesAHowieson/SoftwareArchitecture" \
    --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": "JamesAHowieson/SoftwareArchitecture",
		"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 "JamesAHowieson/SoftwareArchitecture" \
        --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": "JamesAHowieson/SoftwareArchitecture",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JamesAHowieson/SoftwareArchitecture with Docker Model Runner:
```
docker model run hf.co/JamesAHowieson/SoftwareArchitecture
```

Software Architecture Model

Small model that aims to provide advice and guidance in Software Architecture.

Example Prompt: "I am building a new back-end ingestion system in AWS. Please provide some architectural patterns that I could use."

Developed by: James Howieson
Finetuned from model: Qwen2.5-Coder-7B-Instruct

Model Sources

Repository: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct

Uses

Intented to be used as a recommendation agent, not to implement the recommended pattern.

Direct Use

[More Information Needed]

Bias, Risks, and Limitations

Small Parameters mean it will be limited to contextual conversations beyond initial prompts. This should be factored into the inital prompt to ensure you are getting the most our of this agent.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "JamesAHowieson/SoftwareArchitecture"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    device_map="auto",
    dtype="auto",
)

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.21.0

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for JamesAHowieson/SoftwareArchitecture

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(789)

this model

Dataset used to train JamesAHowieson/SoftwareArchitecture

Paper for JamesAHowieson/SoftwareArchitecture

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61