Instructions to use rmdhirr/Anthesis_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rmdhirr/Anthesis_7B with Transformers:

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

pipe = pipeline("text-generation", model="rmdhirr/Anthesis_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rmdhirr/Anthesis_7B")
model = AutoModelForCausalLM.from_pretrained("rmdhirr/Anthesis_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rmdhirr/Anthesis_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rmdhirr/Anthesis_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Anthesis_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rmdhirr/Anthesis_7B

SGLang

How to use rmdhirr/Anthesis_7B 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 "rmdhirr/Anthesis_7B" \
    --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": "rmdhirr/Anthesis_7B",
		"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 "rmdhirr/Anthesis_7B" \
        --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": "rmdhirr/Anthesis_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rmdhirr/Anthesis_7B with Docker Model Runner:
```
docker model run hf.co/rmdhirr/Anthesis_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🏵️ Anthesis_7B

Anthesis_7B is a merge of the following models:

Formatting/Preset

Alpaca works best, but Mistral gives good outputs as well.

Configuration

models:
  - model: ResplendentAI/Paradigm_Shift_7B
    # no parameters necessary for base model
  - model: rmdhirr/Foxglove_7B
    parameters:
      density: 0.60
      weight: 0.40
merge_method: dare_ties
base_model: ResplendentAI/Paradigm_Shift_7B
parameters:
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.97
AI2 Reasoning Challenge (25-Shot)	69.03
HellaSwag (10-Shot)	86.20
MMLU (5-Shot)	62.06
TruthfulQA (0-shot)	68.65
Winogrande (5-shot)	78.93
GSM8k (5-shot)	42.99