Instructions to use budecosystem/genz-13b-v2-ggml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/genz-13b-v2-ggml with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/genz-13b-v2-ggml")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-13b-v2-ggml")
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-13b-v2-ggml")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use budecosystem/genz-13b-v2-ggml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/genz-13b-v2-ggml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/genz-13b-v2-ggml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/budecosystem/genz-13b-v2-ggml

SGLang

How to use budecosystem/genz-13b-v2-ggml 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 "budecosystem/genz-13b-v2-ggml" \
    --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": "budecosystem/genz-13b-v2-ggml",
		"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 "budecosystem/genz-13b-v2-ggml" \
        --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": "budecosystem/genz-13b-v2-ggml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use budecosystem/genz-13b-v2-ggml with Docker Model Runner:
```
docker model run hf.co/budecosystem/genz-13b-v2-ggml
```

GenZ 13B v2 GGML

The instruction finetuned model with 4K input length. The model is finetuned on top of pretrained LLaMa2

Inference

import ctransformers
from ctransformers import AutoModelForCausalLM


model = AutoModelForCausalLM.from_pretrained('budecosystem/genz-13b-v2-ggml', model_type="llama")

prompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: who are you? ASSISTANT: """
print(model(prompt))

Support LM Studio for Mac & Windows users

Use following prompt template

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi, how are you? ASSISTANT:

Check the GitHub for the code -> GenZ

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