Instructions to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="cobrakenji/granite-3b-code-base-Q5_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cobrakenji/granite-3b-code-base-Q5_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cobrakenji/granite-3b-code-base-Q5_K_M-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 cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_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 cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_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 cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M

Use Docker

docker model run hf.co/cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M

LM Studio
Jan

vLLM

How to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cobrakenji/granite-3b-code-base-Q5_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cobrakenji/granite-3b-code-base-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M

SGLang

How to use cobrakenji/granite-3b-code-base-Q5_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 "cobrakenji/granite-3b-code-base-Q5_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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cobrakenji/granite-3b-code-base-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "cobrakenji/granite-3b-code-base-Q5_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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cobrakenji/granite-3b-code-base-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with Ollama:
```
ollama run hf.co/cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
```
Unsloth Desktop
Docker Model Runner
How to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M
```

Lemonade

How to use cobrakenji/granite-3b-code-base-Q5_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cobrakenji/granite-3b-code-base-Q5_K_M-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.granite-3b-code-base-Q5_K_M-GGUF-Q5_K_M

List all available models

lemonade list

Atomic Chat

granite-3b-code-base-Q5_K_M-GGUF / README.md

cobrakenji

Upload README.md with huggingface_hub

756371b verified over 2 years ago

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metadata

license: apache-2.0
library_name: transformers
tags:
  - code
  - granite
  - llama-cpp
  - gguf-my-repo
datasets:
  - codeparrot/github-code-clean
  - bigcode/starcoderdata
  - open-web-math/open-web-math
  - math-ai/StackMathQA
metrics:
  - code_eval
pipeline_tag: text-generation
inference: false
model-index:
  - name: granite-3b-code-base
    results:
      - task:
          type: text-generation
        dataset:
          name: MBPP
          type: mbpp
        metrics:
          - type: pass@1
            value: 36
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: MBPP+
          type: evalplus/mbppplus
        metrics:
          - type: pass@1
            value: 45.1
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: HumanEvalSynthesis(Python)
          type: bigcode/humanevalpack
        metrics:
          - type: pass@1
            value: 36.6
            name: pass@1
          - type: pass@1
            value: 37.2
            name: pass@1
          - type: pass@1
            value: 40.9
            name: pass@1
          - type: pass@1
            value: 26.2
            name: pass@1
          - type: pass@1
            value: 35.4
            name: pass@1
          - type: pass@1
            value: 22
            name: pass@1
          - type: pass@1
            value: 25
            name: pass@1
          - type: pass@1
            value: 18.9
            name: pass@1
          - type: pass@1
            value: 29.9
            name: pass@1
          - type: pass@1
            value: 17.1
            name: pass@1
          - type: pass@1
            value: 26.8
            name: pass@1
          - type: pass@1
            value: 14
            name: pass@1
          - type: pass@1
            value: 18.3
            name: pass@1
          - type: pass@1
            value: 23.2
            name: pass@1
          - type: pass@1
            value: 29.9
            name: pass@1
          - type: pass@1
            value: 24.4
            name: pass@1
          - type: pass@1
            value: 16.5
            name: pass@1
          - type: pass@1
            value: 3.7
            name: pass@1

cobrakenji/granite-3b-code-base-Q5_K_M-GGUF

This model was converted to GGUF format from ibm-granite/granite-3b-code-base 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 cobrakenji/granite-3b-code-base-Q5_K_M-GGUF --model granite-3b-code-base.Q5_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo cobrakenji/granite-3b-code-base-Q5_K_M-GGUF --model granite-3b-code-base.Q5_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-3b-code-base.Q5_K_M.gguf -n 128