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)

# pip install -U transformers accelerate
# 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=256)
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
```

SoftwareArchitecture / README.md

JamesAHowieson

Update README.md

dd17c7c verified 25 days ago

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	---
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
	- lora
	- transformers
	datasets:
	- ajibawa-2023/Software-Architecture
	---

	# 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

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[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

	```python
	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

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

	#### Preprocessing [optional]

	[More Information Needed]


	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

	<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

	[More Information Needed]

	## Evaluation

	<!-- This section describes the evaluation protocols and provides the results. -->

	### Testing Data, Factors & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[More Information Needed]

	#### Factors

	<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

	[More Information Needed]

	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	[More Information Needed]

	### Results

	[More Information Needed]

	#### Summary



	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

	[More Information Needed]

	## Environmental Impact

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- 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]

	<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	[More Information Needed]

	APA:

	[More Information Needed]

	## Glossary [optional]

	<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

	[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