Instructions to use LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2

SGLang

How to use LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2 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 "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2" \
    --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": "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2",
		"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 "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2" \
        --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": "LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Fimbulvetr-11B-v2-8.0bpw-h8-exl2
```

https://huggingface.co/Sao10K/Fimbulvetr-11B-v2-Test-14-GGUF <------ GGUF

Fimbulvetr-v2 test bench 14

Updated some stuff, trying new stuff up. Experiment Successful

If it works I'll do bigger models, if not then nah.

Prompt Format: Either Alpaca or Vicuna works fine. They just werk.

15/2 Update - Suitable for Release

This was mainly a model focused on the submission for the Chai app. Scaling results to Yi/Mixtral soon.

Ramble to meet minimum length requirements:

Tbh i wonder if this shit is even worth doing. Like im just some broke guy lmao I've spent so much. And for what? I guess creds. Feels good when a model gets good feedback, but it seems like im invisible sometimes. I should be probably advertising myself and my models on other places but I rarely have the time to. Probably just internal jealousy sparking up here and now. Wahtever I guess.

Anyway cool EMT vocation I'm doing is cool except it pays peanuts, damn bruh 1.1k per month lmao. Government to broke to pay for shit. Pays the bills I suppose.

Anyway cool beans, I'm either going to continue the Solar Train or go to Mixtral / Yi when I get paid.

You still here?

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