Instructions to use Sao10K/Fimbulvetr-11B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sao10K/Fimbulvetr-11B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Sao10K/Fimbulvetr-11B-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sao10K/Fimbulvetr-11B-v2")
model = AutoModelForCausalLM.from_pretrained("Sao10K/Fimbulvetr-11B-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sao10K/Fimbulvetr-11B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sao10K/Fimbulvetr-11B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sao10K/Fimbulvetr-11B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sao10K/Fimbulvetr-11B-v2

SGLang

How to use Sao10K/Fimbulvetr-11B-v2 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 "Sao10K/Fimbulvetr-11B-v2" \
    --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": "Sao10K/Fimbulvetr-11B-v2",
		"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 "Sao10K/Fimbulvetr-11B-v2" \
        --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": "Sao10K/Fimbulvetr-11B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sao10K/Fimbulvetr-11B-v2 with Docker Model Runner:
```
docker model run hf.co/Sao10K/Fimbulvetr-11B-v2
```

Genuinely, surprisingly good model

#14

by otst - opened Apr 3, 2024

Discussion

otst

Apr 3, 2024

I don't know what you did, but out of the 30 models I've kept (and they're the best of the many others I've tried), somehow both v1 and v2 (even more) outperforms everything else and it makes absolutely no sense. Got all the 7bs and the endless finetunes, bunch of different mixtrals, even 70bs and I always end up just using this model (v1 before) as it just works.

It follows instructions nicely, the code quality is good, is reasonably smart, the output doesn't contain random cyrillics or chinese, when using it to power agents it doesn't spazz out like every single other local model does and usually takes at most couple attempts to output the correct JSON format.

Haven't yet tried out your other finetunes, so don't know if you're the magic ingredient or the dataset you've crafted for fimbulvetr, but ye, hope you keep at it and you should seriously add some "BuyMeACoffee" buttons or something.

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