Instructions to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Dhanishtha-2.0-preview-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Dhanishtha-2.0-preview-i1-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/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Dhanishtha-2.0-preview-i1-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/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Dhanishtha-2.0-preview-i1-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/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Dhanishtha-2.0-preview-i1-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/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dhanishtha-2.0-preview-i1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Dhanishtha-2.0-preview-i1-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 mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/Dhanishtha-2.0-preview-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mradermacher/Dhanishtha-2.0-preview-i1-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

mradermacher commited on Jul 25, 2025

Commit

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1 Parent(s): 3bc0d74

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@@ -1,3 +1,66 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +70,67 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/HelpingAI/Dhanishtha-2.0-preview

+---
+base_model: HelpingAI/Dhanishtha-2.0-preview
+datasets:
+- Abhaykoul/Dhanishtha-R1
+- open-thoughts/OpenThoughts-114k
+- Abhaykoul/Dhanishtha-2.0-SUPERTHINKER
+- Abhaykoul/Dhanishtha-2.0
+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
+library_name: transformers
+license: apache-2.0
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- reasoning
+- intermediate-thinking
+- transformers
+- conversational
+- bilingual
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/HelpingAI/Dhanishtha-2.0-preview
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Dhanishtha-2.0-preview-i1-GGUF).***
+static quants are available at https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-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/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ1_M.gguf) | i1-IQ1_M | 3.9 | mostly desperate |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ2_M.gguf) | i1-IQ2_M | 5.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/Dhanishtha-2.0-preview-i1-GGUF/resolve/main/Dhanishtha-2.0-preview.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
+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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
+<!-- end -->