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

Libraries

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

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Shruti-Soft-2.6b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Shruti-Soft-2.6b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mradermacher/Shruti-Soft-2.6b-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

SGLang

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

Ollama
How to use mradermacher/Shruti-Soft-2.6b-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Shruti-Soft-2.6b-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Shruti-Soft-2.6b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Shruti-Soft-2.6b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Shruti-Soft-2.6b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Shruti-Soft-2.6b-GGUF / README.md

mradermacher

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	---
	base_model: Umranz/Shruti-Soft-2.6b
	language:
	- en
	- hi
	library_name: transformers
	license: apache-2.0
	mradermacher:
	readme_rev: 1
	quantized_by: mradermacher
	tags:
	- conversational
	- roleplay
	- companion
	- character
	- uncensored
	- fine-tuned
	- merged
	- sft
	- chat
	- liquidai
	- lfm
	- lfm2.5
	- chatml
	- pytorch
	- safetensors
	- text-generation
	---
	## About

	<!-- ### quantize_version: 2 -->
	<!-- ### output_tensor_quantised: 1 -->
	<!-- ### convert_type: hf -->
	<!-- ### vocab_type: -->
	<!-- ### tags: -->
	<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
	<!-- ### quants_skip: -->
	<!-- ### skip_mmproj: -->
	static quants of https://huggingface.co/Umranz/Shruti-Soft-2.6b

	<!-- provided-files -->

	*For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Shruti-Soft-2.6b-GGUF).*

	weighted/imatrix quants are available at https://huggingface.co/mradermacher/Shruti-Soft-2.6b-i1-GGUF
	## Usage

	If you are unsure how to use GGUF files, refer to one of [TheBloke's
	READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
	more details, including on how to concatenate multi-part files.

	## Provided Quants

	(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

	\| Link \| Type \| Size/GB \| Notes \|
	\|:-----\|:-----\|--------:\|:------\|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q2_K.gguf) \| Q2_K \| 1.2 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q3_K_S.gguf) \| Q3_K_S \| 1.4 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q3_K_M.gguf) \| Q3_K_M \| 1.5 \| lower quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q3_K_L.gguf) \| Q3_K_L \| 1.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.IQ4_XS.gguf) \| IQ4_XS \| 1.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q4_K_M.gguf) \| Q4_K_M \| 1.8 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q5_K_S.gguf) \| Q5_K_S \| 2.0 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q5_K_M.gguf) \| Q5_K_M \| 2.0 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q6_K.gguf) \| Q6_K \| 2.3 \| very good quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.Q8_0.gguf) \| Q8_0 \| 3.0 \| fast, best quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Shruti-Soft-2.6b-GGUF/resolve/main/Shruti-Soft-2.6b.f16.gguf) \| f16 \| 5.5 \| 16 bpw, overkill \|

	Here is a handy graph by ikawrakow comparing some lower-quality quant
	types (lower is better):

	![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

	And here are Artefact2's thoughts on the matter:
	https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

	## FAQ / Model Request

	See https://huggingface.co/mradermacher/model_requests for some answers to
	questions you might have and/or if you want some other model quantized.

	## Thanks

	I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
	me use its servers and providing upgrades to my workstation to enable
	this work in my free time.

	<!-- end -->