Instructions to use Umranz/Shruti-Soft-2.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Umranz/Shruti-Soft-2.6b with Transformers:

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

pipe = pipeline("text-generation", model="Umranz/Shruti-Soft-2.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Umranz/Shruti-Soft-2.6b")
model = AutoModelForCausalLM.from_pretrained("Umranz/Shruti-Soft-2.6b", 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 Umranz/Shruti-Soft-2.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Umranz/Shruti-Soft-2.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Umranz/Shruti-Soft-2.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Umranz/Shruti-Soft-2.6b

SGLang

How to use Umranz/Shruti-Soft-2.6b 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 "Umranz/Shruti-Soft-2.6b" \
    --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": "Umranz/Shruti-Soft-2.6b",
		"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 "Umranz/Shruti-Soft-2.6b" \
        --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": "Umranz/Shruti-Soft-2.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Umranz/Shruti-Soft-2.6b with Docker Model Runner:
```
docker model run hf.co/Umranz/Shruti-Soft-2.6b
```

Shruti-Soft-2.6b / README.md

Umranz

Update model card for full merged release

537cec7 verified about 1 month ago

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	---
	license: apache-2.0
	base_model: Umranz/LFM2.5-2.6B-heretic
	tags:
	- conversational
	- girlfriend
	- character
	- fine-tuned
	- lfm2.5
	- liquid
	- merged
	language:
	- en
	- hi
	pipeline_tag: text-generation
	---

	# Shruti-Soft-2.6b (Full Standalone Merged)

	Shruti is a full standalone 2.6B parameter uncensored AI girlfriend model fine-tuned from `Umranz/LFM2.5-2.6B-heretic` (LiquidAI hybrid LIV short-conv + GQA architecture) on 5,000 curated ChatML conversations.

	This repository contains the complete merged weights (~5.2 GB safetensors). No separate base model or adapter download is required.

	## Character Profile

	Warm, affectionate, deeply caring. Sweet, playful, emotionally attentive, witty, and supportive. Adapts seamlessly between everyday banter, romance, and mature intimacy. Natural with pet names (baby, babe, handsome, jaan, love).

	## Training Details

	- Framework: LEAP Finetune (LiquidAI official)
	- Method: QLoRA SFT (`r=32`, `alpha=64`, 4 epochs)
	- Source Run: `Umranz/Shruti-Soft-2.6b-run-b` (Winning sweep run with `0.4074` eval loss)
	- Hardware: A100-80GB via Modal.com

	## Quick Start (Transformers)

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Umranz/Shruti-Soft-2.6b"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	messages = [
	{"role": "system", "content": "You are Shruti, a warm, affectionate girlfriend..."},
	{"role": "user", "content": "I had a really long day today..."}
	]

	inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
	outputs = model.generate(inputs, max_new_tokens=200, temperature=0.7, top_p=0.9, do_sample=True)

	print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True).strip())
	```