Instructions to use QuantFactory/granite-3.1-8b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/granite-3.1-8b-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/granite-3.1-8b-instruct-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/granite-3.1-8b-instruct-GGUF",
	filename="granite-3.1-8b-instruct.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/granite-3.1-8b-instruct-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 QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/granite-3.1-8b-instruct-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 QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/granite-3.1-8b-instruct-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 QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/granite-3.1-8b-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": "QuantFactory/granite-3.1-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/granite-3.1-8b-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 "QuantFactory/granite-3.1-8b-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": "QuantFactory/granite-3.1-8b-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 "QuantFactory/granite-3.1-8b-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": "QuantFactory/granite-3.1-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/granite-3.1-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/granite-3.1-8b-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 QuantFactory/granite-3.1-8b-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 QuantFactory/granite-3.1-8b-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 QuantFactory/granite-3.1-8b-instruct-GGUF to start chatting

Pi new

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/granite-3.1-8b-instruct-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": "QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/granite-3.1-8b-instruct-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 QuantFactory/granite-3.1-8b-instruct-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 QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/granite-3.1-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/granite-3.1-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/granite-3.1-8b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-3.1-8b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

jinja2.exceptions.UndefinedError: 'strftime_now' is undefined

by LaferriereJC - opened Jan 18, 2025

Discussion

LaferriereJC

Jan 18, 2025

transformers 4.48.0
transformers-stream-generator 0.0.5

Traceback (most recent call last):
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/queueing.py", line 541, in process_events
response = await route_utils.call_process_api(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
output = await app.get_blocks().process_api(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/blocks.py", line 1928, in process_api
result = await self.call_function(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/blocks.py", line 1526, in call_function
prediction = await utils.async_iteration(iterator)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/utils.py", line 657, in async_iteration
return await iterator.anext()
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/utils.py", line 650, in anext
return await anyio.to_thread.run_sync(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2134, in run_sync_in_worker_thread
return await future
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 851, in run
result = context.run(func, *args)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/utils.py", line 633, in run_sync_iterator_async
return next(iterator)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/gradio/utils.py", line 816, in gen_wrapper
response = next(iterator)
File "/home/user/text-generation-webui/modules/chat.py", line 443, in generate_chat_reply_wrapper
for i, history in enumerate(generate_chat_reply(text, state, regenerate, _continue, loading_message=True, for_ui=True)):
File "/home/user/text-generation-webui/modules/chat.py", line 410, in generate_chat_reply
for history in chatbot_wrapper(text, state, regenerate=regenerate, _continue=_continue, loading_message=loading_message, for_ui=for_ui):
File "/home/user/text-generation-webui/modules/chat.py", line 305, in chatbot_wrapper
stopping_strings = get_stopping_strings(state)
File "/home/user/text-generation-webui/modules/chat.py", line 265, in get_stopping_strings
prefix_bot, suffix_bot = get_generation_prompt(renderer, impersonate=False)
File "/home/user/text-generation-webui/modules/chat.py", line 71, in get_generation_prompt
prompt = renderer(messages=messages)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/jinja2/environment.py", line 1295, in render
self.environment.handle_exception()
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/jinja2/environment.py", line 942, in handle_exception
raise rewrite_traceback_stack(source=source)
File "

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