Instructions to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF",
	filename="granite-20b-code-instruct.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 mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mayank-mishra/granite-20b-code-instruct-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 "mayank-mishra/granite-20b-code-instruct-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": "mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use mayank-mishra/granite-20b-code-instruct-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 "mayank-mishra/granite-20b-code-instruct-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": "mayank-mishra/granite-20b-code-instruct-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 "mayank-mishra/granite-20b-code-instruct-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": "mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-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 mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF to start chatting

Docker Model Runner
How to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-20b-code-instruct-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF

This model was converted to GGUF format from ibm-granite/granite-20b-code-instruct 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.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF --model granite-20b-code-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF --model granite-20b-code-instruct.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.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m granite-20b-code-instruct.Q4_K_M.gguf -n 128

Downloads last month: 17

GGUF

Model size

20B params

Architecture

starcoder

Hardware compatibility

4-bit

Model tree for mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF

Base model

ibm-granite/granite-20b-code-base-8k

Quantized

(13)

this model

Datasets used to train mayank-mishra/granite-20b-code-instruct-Q4_K_M-GGUF

Evaluation results

pass@1 on HumanEvalSynthesis(Python)
self-reported

60.400
pass@1 on HumanEvalSynthesis(Python)
self-reported

53.700
pass@1 on HumanEvalSynthesis(Python)
self-reported

58.500
pass@1 on HumanEvalSynthesis(Python)
self-reported

42.100
pass@1 on HumanEvalSynthesis(Python)
self-reported

45.700
pass@1 on HumanEvalSynthesis(Python)
self-reported

42.700
pass@1 on HumanEvalSynthesis(Python)
self-reported

44.500
pass@1 on HumanEvalSynthesis(Python)
self-reported

42.700