Instructions to use tensorblock/granite-3b-code-instruct-128k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/granite-3b-code-instruct-128k-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tensorblock/granite-3b-code-instruct-128k-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/granite-3b-code-instruct-128k-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/granite-3b-code-instruct-128k-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 tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

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 tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

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 tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/granite-3b-code-instruct-128k-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/granite-3b-code-instruct-128k-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": "tensorblock/granite-3b-code-instruct-128k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

SGLang

How to use tensorblock/granite-3b-code-instruct-128k-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 "tensorblock/granite-3b-code-instruct-128k-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": "tensorblock/granite-3b-code-instruct-128k-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 "tensorblock/granite-3b-code-instruct-128k-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": "tensorblock/granite-3b-code-instruct-128k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/granite-3b-code-instruct-128k-GGUF with Ollama:
```
ollama run hf.co/tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/granite-3b-code-instruct-128k-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 tensorblock/granite-3b-code-instruct-128k-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 tensorblock/granite-3b-code-instruct-128k-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/granite-3b-code-instruct-128k-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use tensorblock/granite-3b-code-instruct-128k-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K
```

Lemonade

How to use tensorblock/granite-3b-code-instruct-128k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/granite-3b-code-instruct-128k-GGUF:Q2_K

Run and chat with the model

lemonade run user.granite-3b-code-instruct-128k-GGUF-Q2_K

List all available models

lemonade list

granite-3b-code-instruct-128k-GGUF / README.md

morriszms

Update README.md

29ab642 verified about 1 year ago

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metadata

pipeline_tag: text-generation
inference: false
license: apache-2.0
datasets:
  - bigcode/commitpackft
  - TIGER-Lab/MathInstruct
  - meta-math/MetaMathQA
  - glaiveai/glaive-code-assistant-v3
  - glaive-function-calling-v2
  - bugdaryan/sql-create-context-instruction
  - garage-bAInd/Open-Platypus
  - nvidia/HelpSteer
  - bigcode/self-oss-instruct-sc2-exec-filter-50k
metrics:
  - code_eval
library_name: transformers
tags:
  - code
  - granite
  - TensorBlock
  - GGUF
base_model: ibm-granite/granite-3b-code-instruct-128k
model-index:
  - name: granite-3b-code-instruct-128k
    results:
      - task:
          type: text-generation
        dataset:
          name: HumanEvalSynthesis (Python)
          type: bigcode/humanevalpack
        metrics:
          - type: pass@1
            value: 53.7
            name: pass@1
            verified: false
          - type: pass@1
            value: 41.4
            name: pass@1
            verified: false
          - type: pass@1
            value: 25.1
            name: pass@1
            verified: false
          - type: pass@1
            value: 26.2
            name: pass@1
            verified: false
      - task:
          type: text-generation
        dataset:
          name: RepoQA (Python@16K)
          type: repoqa
        metrics:
          - type: pass@1 (thresh=0.5)
            value: 48
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 36
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 38
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 39
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 29
            name: pass@1 (thresh=0.5)
            verified: false

ibm-granite/granite-3b-code-instruct-128k - GGUF

This repo contains GGUF format model files for ibm-granite/granite-3b-code-instruct-128k.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

System:
{system_prompt}

Question:
{prompt}

Answer:

Model file specification

Filename	Quant type	File Size	Description
granite-3b-code-instruct-128k-Q2_K.gguf	Q2_K	1.247 GB	smallest, significant quality loss - not recommended for most purposes
granite-3b-code-instruct-128k-Q3_K_S.gguf	Q3_K_S	1.445 GB	very small, high quality loss
granite-3b-code-instruct-128k-Q3_K_M.gguf	Q3_K_M	1.608 GB	very small, high quality loss
granite-3b-code-instruct-128k-Q3_K_L.gguf	Q3_K_L	1.747 GB	small, substantial quality loss
granite-3b-code-instruct-128k-Q4_0.gguf	Q4_0	1.860 GB	legacy; small, very high quality loss - prefer using Q3_K_M
granite-3b-code-instruct-128k-Q4_K_S.gguf	Q4_K_S	1.875 GB	small, greater quality loss
granite-3b-code-instruct-128k-Q4_K_M.gguf	Q4_K_M	1.986 GB	medium, balanced quality - recommended
granite-3b-code-instruct-128k-Q5_0.gguf	Q5_0	2.251 GB	legacy; medium, balanced quality - prefer using Q4_K_M
granite-3b-code-instruct-128k-Q5_K_S.gguf	Q5_K_S	2.251 GB	large, low quality loss - recommended
granite-3b-code-instruct-128k-Q5_K_M.gguf	Q5_K_M	2.316 GB	large, very low quality loss - recommended
granite-3b-code-instruct-128k-Q6_K.gguf	Q6_K	2.666 GB	very large, extremely low quality loss
granite-3b-code-instruct-128k-Q8_0.gguf	Q8_0	3.451 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/granite-3b-code-instruct-128k-GGUF --include "granite-3b-code-instruct-128k-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/granite-3b-code-instruct-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'