Instructions to use webkul/unopim-devdocs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webkul/unopim-devdocs with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("webkul/unopim-devdocs")
model = AutoModelForCausalLM.from_pretrained("webkul/unopim-devdocs")
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 webkul/unopim-devdocs with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/webkul/unopim-devdocs

SGLang

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

Unsloth Studio

How to use webkul/unopim-devdocs with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for webkul/unopim-devdocs to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for webkul/unopim-devdocs to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for webkul/unopim-devdocs to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="webkul/unopim-devdocs",
    max_seq_length=2048,
)

Docker Model Runner
How to use webkul/unopim-devdocs with Docker Model Runner:
```
docker model run hf.co/webkul/unopim-devdocs
```

🚀 Fine-tuned Gemma 3 Model (4B, 4-bit) by Webkul

This repository contains a fine-tuned version of Unsloth's gemma-3-4b-it model, optimized for lightweight 4-bit inference and instruction tuning using Hugging Face's TRL and Unsloth's speed-optimized framework.

What is UnoPim

UnoPim is an open-source Product Information Management (PIM) system built on the Laravel framework. It helps businesses organize, manage, and enrich their product information in one central repository.

🔧 Model Details

Base Model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
Fine-tuned By: Webkul
License: Apache 2.0
Language: English (en)
Model Size: 4B parameters (4-bit quantized)
Frameworks Used: Unsloth, Hugging Face Transformers, TRL

📚 Fine-tuning Dataset

This model was fine-tuned on unopim dev documentation available at https://devdocs.unopim.com/, focusing on structured software documentation and developer support content.

💡 Intended Use

Conversational AI assistants trained on UnoPIM developer docs
API documentation question answering
Developer tools and chatbot integrations
Contextual helpdesk or onboarding bots for UnoPIM products

🧪 How to Use

You can use this model with the Hugging Face transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "webkul/gemma-3-4b-it-unopim-docs"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "How do I integrate the UnoPIM API for product syncing?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📄 License This model is licensed under the Apache License 2.0.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for webkul/unopim-devdocs

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Finetuned

(1121)

this model