Instructions to use hharsha/agentic-github-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hharsha/agentic-github-tagger with Transformers:

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

pipe = pipeline("text-generation", model="hharsha/agentic-github-tagger")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("hharsha/agentic-github-tagger")
model = AutoModelForSeq2SeqLM.from_pretrained("hharsha/agentic-github-tagger", device_map="auto")

PEFT
How to use hharsha/agentic-github-tagger with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hharsha/agentic-github-tagger with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hharsha/agentic-github-tagger"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hharsha/agentic-github-tagger",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hharsha/agentic-github-tagger

SGLang

How to use hharsha/agentic-github-tagger 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 "hharsha/agentic-github-tagger" \
    --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": "hharsha/agentic-github-tagger",
		"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 "hharsha/agentic-github-tagger" \
        --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": "hharsha/agentic-github-tagger",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hharsha/agentic-github-tagger with Docker Model Runner:
```
docker model run hf.co/hharsha/agentic-github-tagger
```

agentic-github-tagger / README.md

hharsha

Fix usage for transformers without text2text-generation pipeline task

29a3108 verified 1 day ago

preview code

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2.74 kB

	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	base_model: google-t5/t5-small
	tags:
	- peft
	- t5
	- agents
	- rag
	- llmops
	- lora
	- text2text-generation
	datasets:
	- hharsha/agentic-github-meta
	pipeline_tag: text2text-generation
	widget:
	- text: multi-agent platform with RAG, MCP, and observability
	- text: looking for RAG hybrid search recall@k and reranking
	- text: FastAPI Next.js agent backend with Docker Compose
	---

	# agentic-github-tagger

	Lightweight text2text tag generator for agentic AI / RAG / LLMOps GitHub-style
	descriptions. Fine-tuned from [`google-t5/t5-small`](https://huggingface.co/google-t5/t5-small)
	with PEFT LoRA (r=16, alpha=32, dropout=0.05, target_modules `q`,`v`) on
	[`hharsha/agentic-github-meta`](https://huggingface.co/datasets/hharsha/agentic-github-meta),
	then merged so full small weights load on free CPU.

	> ~60M-param T5-small tagger — not a 7B chat demo. Free Hub + CPU friendly.

	## Usage

	```python
	from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

	model_id = "hharsha/agentic-github-tagger"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

	text = "multi-agent platform with RAG, MCP, and observability"
	ids = tok(text, return_tensors="pt")
	out = model.generate(**ids, max_new_tokens=64, num_beams=4)
	print(tok.decode(out[0], skip_special_tokens=True))
	```

	On older `transformers` that still register the task, this also works:

	```python
	from transformers import pipeline
	pipe = pipeline("text2text-generation", model="hharsha/agentic-github-tagger")
	print(pipe("multi-agent platform with RAG, MCP, and observability")[0]["generated_text"])
	```

	Sample output from this training run:

	```
	multi-agent, multi-agent, observability, rag, mCP, observability
	```

	## Training

	\| \| \|
	\|---\|---\|
	\| Base \| `google-t5/t5-small` \|
	\| Method \| PEFT LoRA then merge \|
	\| r / alpha / dropout \| 16 / 32 / 0.05 \|
	\| target_modules \| q, v \|
	\| Epochs \| 3 (CPU) \|
	\| Batch size \| 8 \|
	\| Dataset \| [`hharsha/agentic-github-meta`](https://huggingface.co/datasets/hharsha/agentic-github-meta) (687 rows; 600 used for train) \|

	## Links

	- Dataset: [`hharsha/agentic-github-meta`](https://huggingface.co/datasets/hharsha/agentic-github-meta)
	- Showcase: [`hharsha/agentic-systems-showcase`](https://huggingface.co/datasets/hharsha/agentic-systems-showcase)
	- Studio: [https://agentic-systems-studio.com](https://agentic-systems-studio.com)
	- GitHub: [https://github.com/hharsha98](https://github.com/hharsha98)

	## Intended use / limits

	Auto-suggest comma-separated tags for agentic / RAG / LLMOps project listings.
	Small model; tags can repeat or be incomplete. Not for safety-critical labeling.