Instructions to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF",
	filename="dhanishtha-2.0-preview-0825-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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 Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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 Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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": "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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 "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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": "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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 "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-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": "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF 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 Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF 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 Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF to start chatting

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF / README.md

Orion-zhen

Upload README.md with huggingface_hub

f6ce3dc verified 10 months ago

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metadata

language:
  - en
  - hi
  - zh
  - es
  - fr
  - de
  - ja
  - ko
  - ar
  - pt
  - ru
  - it
  - nl
  - tr
  - pl
  - sv
  - da
  - 'no'
  - fi
  - he
  - th
  - vi
  - id
  - ms
  - tl
  - sw
  - yo
  - zu
  - am
  - bn
  - gu
  - kn
  - ml
  - mr
  - ne
  - or
  - pa
  - ta
  - te
  - ur
  - multilingual
license: apache-2.0
base_model: HelpingAI/Dhanishtha-2.0-preview-0825
tags:
  - reasoning
  - intermediate-thinking
  - transformers
  - conversational
  - bilingual
  - llama-cpp
  - gguf-my-repo
datasets:
  - Abhaykoul/Dhanishtha-R1
  - open-thoughts/OpenThoughts-114k
  - Abhaykoul/Dhanishtha-2.0-SUPERTHINKER
  - Abhaykoul/Dhanishtha-2.0
library_name: transformers
pipeline_tag: text-generation
widget:
  - text: >-
      Solve this riddle step by step: I am taken from a mine, and shut up in a
      wooden case, from which I am never released, and yet I am used by almost
      everybody. What am I?
    example_title: Complex Riddle Solving
  - text: >-
      Explain the philosophical implications of artificial consciousness and
      think through different perspectives.
    example_title: Philosophical Reasoning
  - text: >-
      Help me understand quantum mechanics, but take your time to think through
      the explanation.
    example_title: Educational Explanation

Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF

This model was converted to GGUF format from HelpingAI/Dhanishtha-2.0-preview-0825 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF --hf-file dhanishtha-2.0-preview-0825-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF --hf-file dhanishtha-2.0-preview-0825-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF --hf-file dhanishtha-2.0-preview-0825-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Orion-zhen/Dhanishtha-2.0-preview-0825-Q4_K_M-GGUF --hf-file dhanishtha-2.0-preview-0825-q4_k_m.gguf -c 2048