Instructions to use inflatebot/helide-alpha-r5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inflatebot/helide-alpha-r5 with Transformers:

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

pipe = pipeline("text-generation", model="inflatebot/helide-alpha-r5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inflatebot/helide-alpha-r5")
model = AutoModelForCausalLM.from_pretrained("inflatebot/helide-alpha-r5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inflatebot/helide-alpha-r5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inflatebot/helide-alpha-r5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/helide-alpha-r5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inflatebot/helide-alpha-r5

SGLang

How to use inflatebot/helide-alpha-r5 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 "inflatebot/helide-alpha-r5" \
    --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": "inflatebot/helide-alpha-r5",
		"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 "inflatebot/helide-alpha-r5" \
        --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": "inflatebot/helide-alpha-r5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inflatebot/helide-alpha-r5 with Docker Model Runner:
```
docker model run hf.co/inflatebot/helide-alpha-r5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

"Helide" (say HE-lied) is an ion of helium -- famously a very unreactive element, which doesn't form ions in most conditions.

GGUFs available from mradermacher (appreciate it!!)

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

An experimental merge of the legendary L3-8B-Stheno with Fizzarolli's Rosier. The aim is to improve Stheno's "ball-rolling" capabilities and reduce its awkwardness with more niche content. For a first go, I'm surprised at how well it's doing so far, but given that this is literally my first LLM project ever, probably temper your expectations.

Since R1: Changed to task-arithmetic. Snazzy new model card image.

Since R2: Fixed unnecessary conversion.

Since R3: Tweaked ratios, Rosier's influence cut in half.

Since R4: Scrubbin' it down. +0.08 to Rosier (pre-normalization). Closing in on a good ratio.

Merge Method

This model was merged using the task arithmetic merge method using NousResearch/Meta-Llama-3-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Sao10K/L3-8B-Stheno-v3.2
    parameters:
      weight: 0.5
  - model: Fizzarolli/L3-8b-Rosier-v1
    parameters:
      weight: 0.33

merge_method: task_arithmetic
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  normalize: true
dtype: bfloat16