Instructions to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Mungert/granite-3.1-3b-a800m-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mungert/granite-3.1-3b-a800m-instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mungert/granite-3.1-3b-a800m-instruct-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Use Docker

docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

LM Studio
Jan

vLLM

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mungert/granite-3.1-3b-a800m-instruct-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": "Mungert/granite-3.1-3b-a800m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

SGLang

How to use Mungert/granite-3.1-3b-a800m-instruct-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 "Mungert/granite-3.1-3b-a800m-instruct-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": "Mungert/granite-3.1-3b-a800m-instruct-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 "Mungert/granite-3.1-3b-a800m-instruct-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": "Mungert/granite-3.1-3b-a800m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Ollama:
```
ollama run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
```

Unsloth Studio

How to use Mungert/granite-3.1-3b-a800m-instruct-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 Mungert/granite-3.1-3b-a800m-instruct-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/granite-3.1-3b-a800m-instruct-GGUF to start chatting

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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": "Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/granite-3.1-3b-a800m-instruct-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Run Hermes

hermes

OpenClaw new

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 "Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16" \
  --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"

Docker Model Runner
How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
```

Lemonade

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Run and chat with the model

lemonade run user.granite-3.1-3b-a800m-instruct-GGUF-BF16

List all available models

lemonade list

Atomic Chat

Mungert commited on Sep 24, 2025

Commit

dcd79ee

verified ·

0 Parent(s):

Super-squash history to reclaim storage

Browse files

Files changed (24) hide show

.gitattributes +71 -0
README.md +419 -0
granite-3.1-3b-a800m-instruct-bf16.gguf +3 -0
granite-3.1-3b-a800m-instruct-bf16_q8_0.gguf +3 -0
granite-3.1-3b-a800m-instruct-f16_q8_0.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq3_m.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq3_s.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq3_xs.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq3_xxs.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq4_nl.gguf +3 -0
granite-3.1-3b-a800m-instruct-iq4_xs.gguf +3 -0
granite-3.1-3b-a800m-instruct-q2_k_m.gguf +3 -0
granite-3.1-3b-a800m-instruct-q2_k_s.gguf +3 -0
granite-3.1-3b-a800m-instruct-q3_k_m.gguf +3 -0
granite-3.1-3b-a800m-instruct-q3_k_s.gguf +3 -0
granite-3.1-3b-a800m-instruct-q4_0.gguf +3 -0
granite-3.1-3b-a800m-instruct-q4_1.gguf +3 -0
granite-3.1-3b-a800m-instruct-q5_0.gguf +3 -0
granite-3.1-3b-a800m-instruct-q5_1.gguf +3 -0
granite-3.1-3b-a800m-instruct-q5_k_m.gguf +3 -0
granite-3.1-3b-a800m-instruct-q5_k_s.gguf +3 -0
granite-3.1-3b-a800m-instruct-q6_k_m.gguf +3 -0
granite-3.1-3b-a800m-instruct-q8_0.gguf +3 -0
granite-3.1-3b-a800m-instruct.imatrix +3 -0

.gitattributes ADDED Viewed

	@@ -0,0 +1,71 @@

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-f16.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-f16_q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-bf16_q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-f16_q6_k.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-bf16_q6_k.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q2_k_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q3_k_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_k_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q6_k_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q2_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q2_k_s.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q3_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q3_k_s.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_k_s.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q6_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q4_1.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q4_0_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q4_1_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_1.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_0_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q5_1_l.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq3_xs.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq3_xxs.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq3_s.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq3_m.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq4_xs.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-iq4_nl.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct.imatrix filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-bf16_q4_k.gguf.tmp filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-bf16.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-f16_q4_k.gguf.tmp filter=lfs diff=lfs merge=lfs -text
+granite-3.1-3b-a800m-instruct-q4_k_l.gguf.tmp filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,419 @@

+---
+pipeline_tag: text-generation
+inference: false
+license: apache-2.0
+library_name: transformers
+tags:
+- language
+- granite-3.1
+base_model:
+- ibm-granite/granite-3.1-3b-a800m-base
+---
+# <span style="color: #7FFF7F;">granite-3.1-3b-a800m-instruct GGUF Models</span>
+## <span style="color: #7F7FFF;">Model Generation Details</span>
+This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`0a5a3b5c`](https://github.com/ggerganov/llama.cpp/commit/0a5a3b5cdfd887cf0f8e09d9ff89dee130cfcdde).
+---
+<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
+  Click here to get info on choosing the right GGUF model format
+</a>
+---
+<!--Begin Original Model Card-->
+# Granite-3.1-3B-A800M-Instruct
+**Model Summary:**
+Granite-3.1-3B-A800M-Instruct is a 3B parameter long-context instruct model finetuned from Granite-3.1-3B-A800M-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.
+- **Developers:** Granite Team, IBM
+- **GitHub Repository:** [ibm-granite/granite-3.1-language-models](https://github.com/ibm-granite/granite-3.1-language-models)
+- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
+- **Paper:** [Granite 3.1 Language Models (coming soon)](https://huggingface.co/collections/ibm-granite/granite-31-language-models-6751dbbf2f3389bec5c6f02d)
+- **Release Date**: December 18th, 2024
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+**Supported Languages:**
+English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages.
+**Intended Use:**
+The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
+*Capabilities*
+* Summarization
+* Text classification
+* Text extraction
+* Question-answering
+* Retrieval Augmented Generation (RAG)
+* Code related tasks
+* Function-calling tasks
+* Multilingual dialog use cases
+* Long-context tasks including long document/meeting summarization, long document QA, etc.
+**Generation:**
+This is a simple example of how to use Granite-3.1-3B-A800M-Instruct model.
+Install the following libraries:
+```shell
+pip install torch torchvision torchaudio
+pip install accelerate
+pip install transformers
+```
+Then, copy the snippet from the section that is relevant for your use case.
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "auto"
+model_path = "ibm-granite/granite-3.1-3b-a800m-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# drop device_map if running on CPU
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
+model.eval()
+# change input text as desired
+chat = [
+    { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
+]
+chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+# tokenize the text
+input_tokens = tokenizer(chat, return_tensors="pt").to(device)
+# generate output tokens
+output = model.generate(**input_tokens,
+                        max_new_tokens=100)
+# decode output tokens into text
+output = tokenizer.batch_decode(output)
+# print output
+print(output)
+```
+**Evaluation Results:**
+<table>
+  <caption><b>HuggingFace Open LLM Leaderboard V1</b></caption>
+<thead>
+  <tr>
+    <th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">ARC-Challenge</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">Hellaswag</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">MMLU</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">TruthfulQA</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">Winogrande</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">GSM8K</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">Avg</th>
+  </tr></thead>
+  <tbody>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Granite-3.1-8B-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">62.62</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">84.48</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">65.34</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">66.23</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">75.37</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">73.84</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">71.31</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-2B-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">54.61</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">75.14</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">55.31</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">59.42</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.48</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">52.76</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">60.79</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: #2D2D2D;">Granite-3.1-3B-A800M-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">50.42</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">73.01</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">52.19</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">49.71</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">64.87</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">48.97</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">56.53</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-1B-A400M-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">42.66</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">65.97</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">26.13</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">46.77</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">62.35</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">33.88</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">46.29</td>
+  </tr>
+</tbody></table>
+<table>
+  <caption><b>HuggingFace Open LLM Leaderboard V2</b></caption>
+<thead>
+  <tr>
+    <th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">IFEval</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">BBH</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">MATH Lvl 5</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">GPQA</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">MUSR</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">MMLU-Pro</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">Avg</th>
+  </tr></thead>
+  <tbody>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Granite-3.1-8B-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">72.08</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">34.09</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">21.68</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8.28</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">19.01</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">28.19</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">30.55</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-2B-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">62.86</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">21.82</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">11.33</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">5.26</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">4.87</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">20.21</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">21.06</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: #2D2D2D;">Granite-3.1-3B-A800M-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">55.16</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">16.69</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">10.35</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">5.15</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">2.51</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">12.75</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;">17.1</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-1B-A400M-Instruct</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">46.86</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">6.18</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">4.08</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">0</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">0.78</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">2.41</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">10.05</td>
+  </tr>
+</tbody></table>
+**Model Architecture:**
+Granite-3.1-3B-A800M-Instruct is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA and RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
+<table>
+<thead>
+  <tr>
+    <th style="text-align:left; background-color: #001d6c; color: white;">Model</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">2B Dense</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">8B Dense</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">1B MoE</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">3B MoE</th>
+  </tr></thead>
+<tbody>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Embedding size</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">2048</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">4096</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">1024</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">1536</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of layers</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">40</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">40</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">24</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">32</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Attention head size</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of attention heads</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">16</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">24</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of KV heads</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">8</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP hidden size</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8192</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">12800</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">512</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">512</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP activation</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">SwiGLU</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of experts</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">—</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">—</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">40</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">MoE TopK</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">—</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">—</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">8</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Initialization std</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">0.1</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">0.1</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">0.1</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">0.1</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Sequence length</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">128K</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;">Position embedding</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">RoPE</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;"># Parameters</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">2.5B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8.1B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">1.3B</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">3.3B</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;"># Active parameters</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">2.5B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8.1B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">400M</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">800M</td>
+  </tr>
+  <tr>
+    <td style="text-align:left; background-color: #FFFFFF; color: black;"># Training tokens</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">12T</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">12T</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">10T</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">10T</td>
+  </tr>
+</tbody></table>
+**Training Data:**
+Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities including long-context tasks, and (3) very small amounts of human-curated data. A detailed attribution of datasets can be found in the [Granite 3.0 Technical Report](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf), [Granite 3.1 Technical Report (coming soon)](https://huggingface.co/collections/ibm-granite/granite-31-language-models-6751dbbf2f3389bec5c6f02d), and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf).
+**Infrastructure:**
+We train Granite 3.1 Language Models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
+**Ethical Considerations and Limitations:**
+Granite 3.1 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.
+**Resources**
+- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
+- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
+- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
+<!-- ## Citation
+```
+@misc{granite-models,
+  author = {author 1, author2, ...},
+  title = {},
+  journal = {},
+  volume = {},
+  year = {2024},
+  url = {https://arxiv.org/abs/0000.00000},
+}
+``` -->
+<!--End Original Model Card-->
+---
+# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
+Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:
+👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)
+The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)
+💬 **How to test**:
+ Choose an **AI assistant type**:
+   - `TurboLLM` (GPT-4.1-mini)
+   - `HugLLM` (Hugginface Open-source models)
+   - `TestLLM` (Experimental CPU-only)
+### **What I’m Testing**
+I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
+- **Function calling** against live network services
+- **How small can a model go** while still handling:
+  - Automated **Nmap security scans**
+  - **Quantum-readiness checks**
+  - **Network Monitoring tasks**
+🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
+- ✅ **Zero-configuration setup**
+- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
+- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
+### **Other Assistants**
+🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
+- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
+- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
+- **Real-time network diagnostics and monitoring**
+- **Security Audits**
+- **Penetration testing** (Nmap/Metasploit)
+🔵 **HugLLM** – Latest Open-source models:
+- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
+### 💡 **Example commands you could test**:
+1. `"Give me info on my websites SSL certificate"`
+2. `"Check if my server is using quantum safe encyption for communication"`
+3. `"Run a comprehensive security audit on my server"`
+4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!
+### Final Word
+I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.
+If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
+I'm also open to job opportunities or sponsorship.
+Thank you! 😊

granite-3.1-3b-a800m-instruct-bf16.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:401ab383a5eaa5013d167659ec05004c0e7d42147c95f4e815df989e1d9355f3
+size 6603463648

granite-3.1-3b-a800m-instruct-bf16_q8_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d5bb91a20b39a54e3b498220a037ef01d4c465dcd247d5cba53d817219faf484
+size 3677936608

granite-3.1-3b-a800m-instruct-f16_q8_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:9f7b965ba2490ca7e281ecd067a91c5487d4b08cad6a0b6293c265c140841ca5
+size 3677936608

granite-3.1-3b-a800m-instruct-iq3_m.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:05019a5430a4c1787cc4213efeaccb642d04cd51141e5f3fcd75439d1481b420
+size 1450706272

granite-3.1-3b-a800m-instruct-iq3_s.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:84b7063b0de62ff8fc33c1a3423296d84ffa0a4b4042ea0538d8aab2ce156497
+size 1450706272

granite-3.1-3b-a800m-instruct-iq3_xs.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:01cd34c78bb5952acad3df1f0090c72b881e60ea810af505e3f15c6b3d39d128
+size 1371178336

granite-3.1-3b-a800m-instruct-iq3_xxs.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:70119cd2c9e41cfd41cd3cebe9f4dba893cbee6bf9c54c72af0d008673f2843d
+size 1329497440

granite-3.1-3b-a800m-instruct-iq4_nl.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:e1cf857b795615bcb1597998b4a24b08090eb9877595eee09340652971630e78
+size 1883883488

granite-3.1-3b-a800m-instruct-iq4_xs.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:110208fd8335469eb574df14adc22dc93eb741f0cd82c4c2d77b5e72134e64b3
+size 1783220192

granite-3.1-3b-a800m-instruct-q2_k_m.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:96aff7359e99fca18d882cc03db03a717b17caad35e4d82b4e29371c5b99f035
+size 1254442976

granite-3.1-3b-a800m-instruct-q2_k_s.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:0384e949abf527861124e742024c5830cd92d5b71f0ebba1b2118ae97768bff6
+size 1167738208

granite-3.1-3b-a800m-instruct-q3_k_m.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:1fce6f78dd00601669ad940ae05564e2acf279845b64107835b4e2337d97a3a5
+size 1644611552

granite-3.1-3b-a800m-instruct-q3_k_s.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:980a3bc6abbdf62c520596b81a564e34cc1e130dcea2c1e9f812ff9715ecd536
+size 1534313824

granite-3.1-3b-a800m-instruct-q4_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:789e082b04ab7646bcfcf788d7f4a184230850e9c2e46582d0cfb80b53216b83
+size 1864418080

granite-3.1-3b-a800m-instruct-q4_1.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:0896e8d8a8d08c2292895c43417dba690968ef9049f5c806df1399fa3a0eacf3
+size 2070463552

granite-3.1-3b-a800m-instruct-q5_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:6a2ee9bb1310eac20d71ded18f5b35ab2000c10dcb1a9bb2ada4a8ac366c1a55
+size 2276509024

granite-3.1-3b-a800m-instruct-q5_1.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:37fb5677d4997dd73cdef1dfe0d768a5fb71652bbca529507333fbe1ff38d4cb
+size 2482554496

granite-3.1-3b-a800m-instruct-q5_k_m.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:eec3b3a465cc1aefb3c3fafad335b7da18c89d19388f19fe3a1c09d29893c70c
+size 2424408032

granite-3.1-3b-a800m-instruct-q5_k_s.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:4f8a3e04ddfa1eb3b262c211ab334a52be9de2750372f25dc275cb92cef10b8f
+size 2390149088

granite-3.1-3b-a800m-instruct-q6_k_m.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:0f6a76525b14be05320313112dabb3bf059b923c5206a85139af3fb7aad1ae42
+size 2714355680

granite-3.1-3b-a800m-instruct-q8_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9c1e5fb32397fd44a2b1d9d5469142f1e98d67e2701b9666891c9fc93907d1
+size 3512781568

granite-3.1-3b-a800m-instruct.imatrix ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3fb33a0123fc0055e42be161d5bbbbfc13de561e83bddb9093c94962751a3ab9
+size 18195136