Instructions to use mpasila/Alpacazord-Viking-7B-exl2-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/Alpacazord-Viking-7B-exl2-4bpw with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/Alpacazord-Viking-7B-exl2-4bpw")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/Alpacazord-Viking-7B-exl2-4bpw")
model = AutoModelForCausalLM.from_pretrained("mpasila/Alpacazord-Viking-7B-exl2-4bpw", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mpasila/Alpacazord-Viking-7B-exl2-4bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mpasila/Alpacazord-Viking-7B-exl2-4bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mpasila/Alpacazord-Viking-7B-exl2-4bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mpasila/Alpacazord-Viking-7B-exl2-4bpw

SGLang

How to use mpasila/Alpacazord-Viking-7B-exl2-4bpw 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 "mpasila/Alpacazord-Viking-7B-exl2-4bpw" \
    --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": "mpasila/Alpacazord-Viking-7B-exl2-4bpw",
		"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 "mpasila/Alpacazord-Viking-7B-exl2-4bpw" \
        --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": "mpasila/Alpacazord-Viking-7B-exl2-4bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mpasila/Alpacazord-Viking-7B-exl2-4bpw with Docker Model Runner:
```
docker model run hf.co/mpasila/Alpacazord-Viking-7B-exl2-4bpw
```

Alpacazord-Viking-7B-exl2-4bpw / README.md

mpasila

Update README.md

b5bf6d6 verified over 2 years ago

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metadata

language:
  - fi
base_model: LumiOpen/Viking-7B
license: apache-2.0
datasets:
  - mpasila/Alpacazord-V1

This is an ExLlamaV2 quantized model in 4bpw of mpasila/Alpacazord-Viking-7B using the default calibration dataset.

Original Model card:

Model Card for Alpacazord-Viking-7B

This is a merge of mpasila/Alpacazord-Viking-LoRA-7B.

LoRA trained with text-generation-webui in 4-bit using LumiOpen/Viking-7B as the base model for 1 epoch. Dataset used with the LoRA is mpasila/Alpacazord-V1.

It uses Alpaca format like so:

{
    "instruction,output": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
    "instruction,input,output": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}

Merged using this Colab notebook. It might not be the best way to merge a quantized LoRA on to a float16 model but I just wanted to quickly do something. You can try merging it better if you want.

Evaluation

Model	Size	Type	FIN-bench (score)
mpasila/Alpacazord-Viking-7B	7B	Instruct
mpasila/Finnish-Viking-Alpaca-V1-7B	7B	Instruct	0.3943
mpasila/NordicAlpaca-Finnish-V1-7B	7B	Instruct	0.3891
Finnish-NLP/llama-7b-finnish-instruct-v0.1	7B	Instruct	0.4365
Finnish-NLP/llama-7b-finnish-instruct-v0.2	7B	Instruct	0.3993
Finnish-NLP/llama-7b-finnish	7B	Base	0.2350
LumiOpen/Viking-7B (1000B)	7B	Base	0.3721
HPLT/gpt-7b-nordic-prerelease	7B	Base	0.3169

Source

FIN-bench scores:

Will add later. And possibly other evals?????

Framework versions

PEFT 0.8.2