Instructions to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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 sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

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 sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

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 sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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": "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

SGLang

How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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 "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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": "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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 "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-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": "sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with Ollama:
```
ollama run hf.co/sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0
```

Lemonade

How to use sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.granite-8b-code-instruct-128k-Q8_0-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

granite-8b-code-instruct-128k-Q8_0-GGUF / README.md

sdalemorrey

Upload README.md with huggingface_hub

d128808 verified about 2 years ago

preview code

Raw

History Blame Contribute Delete

3.47 kB

	---
	base_model: ibm-granite/granite-8b-code-instruct-128k
	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
	library_name: transformers
	license: apache-2.0
	metrics:
	- code_eval
	pipeline_tag: text-generation
	tags:
	- code
	- granite
	- llama-cpp
	- gguf-my-repo
	inference: false
	model-index:
	- name: granite-8B-Code-instruct-128k
	results:
	- task:
	type: text-generation
	dataset:
	name: HumanEvalSynthesis (Python)
	type: bigcode/humanevalpack
	metrics:
	- type: pass@1
	value: 62.2
	name: pass@1
	verified: false
	- type: pass@1
	value: 51.4
	name: pass@1
	verified: false
	- type: pass@1
	value: 38.9
	name: pass@1
	verified: false
	- type: pass@1
	value: 38.3
	name: pass@1
	verified: false
	- task:
	type: text-generation
	dataset:
	name: RepoQA (Python@16K)
	type: repoqa
	metrics:
	- type: pass@1 (thresh=0.5)
	value: 73.0
	name: pass@1 (thresh=0.5)
	verified: false
	- type: pass@1 (thresh=0.5)
	value: 37.0
	name: pass@1 (thresh=0.5)
	verified: false
	- type: pass@1 (thresh=0.5)
	value: 73.0
	name: pass@1 (thresh=0.5)
	verified: false
	- type: pass@1 (thresh=0.5)
	value: 62.0
	name: pass@1 (thresh=0.5)
	verified: false
	- type: pass@1 (thresh=0.5)
	value: 63.0
	name: pass@1 (thresh=0.5)
	verified: false
	---

	# sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF
	This model was converted to GGUF format from [`ibm-granite/granite-8b-code-instruct-128k`](https://huggingface.co/ibm-granite/granite-8b-code-instruct-128k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/ibm-granite/granite-8b-code-instruct-128k) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF --hf-file granite-8b-code-instruct-128k-q8_0.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF --hf-file granite-8b-code-instruct-128k-q8_0.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF --hf-file granite-8b-code-instruct-128k-q8_0.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo sdalemorrey/granite-8b-code-instruct-128k-Q8_0-GGUF --hf-file granite-8b-code-instruct-128k-q8_0.gguf -c 2048
	```