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
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

agentic-github-tagger

Lightweight text2text tag generator for agentic AI / RAG / LLMOps GitHub-style descriptions. Fine-tuned from google-t5/t5-small with PEFT LoRA (r=16, alpha=32, dropout=0.05, target_modules q,v) on 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

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:

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` (687 rows; 600 used for train)

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.

Downloads last month: 107

Safetensors

Model size

60.5M params

Tensor type

F32

Model tree for hharsha/agentic-github-tagger

Base model

google-t5/t5-small

Adapter

(77)

this model

hharsha
/

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agentic-github-tagger

Usage

Training

Links

Intended use / limits

Model tree for hharsha/agentic-github-tagger

Dataset used to train hharsha/agentic-github-tagger