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
```

extremely good model

#23

by Utochi - opened May 22, 2024

Discussion

Utochi

May 22, 2024

this model has exceeded my expectations and performs better than the recent L3 model breakthroughs. a diamond in the rough so to speak, i didn't expect to find a small sized model that wasn't L3 that was capable of performing as well as QuartetAnemoi.
this model performs extremely well with large context character cards though it does have its quirks, it does need corrected on occasion but you can really tell it does it's best to follow rules that are neatly laid out.
9 out of 10 stars.
settings used:
temperature 1.2
top p 1
top k 30
repeat penalty 1.05
repeat penalty tokens at 250
prompt template Model default
UI used faraday/backyardAI

boxman222

May 25, 2024

Been using this on a IRC chatbot and so far it is only model that can handle interacting with many different people without getting confused.

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