Instructions to use tachyphylaxis/GoldDiamondGold-L33-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tachyphylaxis/GoldDiamondGold-L33-70b with Transformers:

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

pipe = pipeline("text-generation", model="tachyphylaxis/GoldDiamondGold-L33-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tachyphylaxis/GoldDiamondGold-L33-70b")
model = AutoModelForCausalLM.from_pretrained("tachyphylaxis/GoldDiamondGold-L33-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tachyphylaxis/GoldDiamondGold-L33-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tachyphylaxis/GoldDiamondGold-L33-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tachyphylaxis/GoldDiamondGold-L33-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tachyphylaxis/GoldDiamondGold-L33-70b

SGLang

How to use tachyphylaxis/GoldDiamondGold-L33-70b 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 "tachyphylaxis/GoldDiamondGold-L33-70b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tachyphylaxis/GoldDiamondGold-L33-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tachyphylaxis/GoldDiamondGold-L33-70b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tachyphylaxis/GoldDiamondGold-L33-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tachyphylaxis/GoldDiamondGold-L33-70b with Docker Model Runner:
```
docker model run hf.co/tachyphylaxis/GoldDiamondGold-L33-70b
```

GoldDiamondGold-70b

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

Motivation

Inspired from the sapphire model by BruhzWater, the base for this is cogito.
Rest of the models is what I think would be good for a SCE attempt. Basically combining all the best bits in each model and see what I get out of this.

There's 3 interesting models that I highlighted in the previous merge that went into this too.

Vibes

Seems OK. I think it's better than the previous model. That felt super sloppy.

Merge Details

Merge Method

This model was merged using the SCE merge method using deepcogito/cogito-v2-preview-llama-70B 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:
  # Mirai is Mirai.
  - model: Blackroot/Mirai-3.0-70B
  # Narration
  - model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0

  # Claude 3 Sonnet/Opus prose style and quality
  - model: Doctor-Shotgun/L3.3-70B-Magnum-Diamond
  # "For the *Action*"?
  - model: marcelbinz/Llama-3.1-Centaur-70B

  # Better writing style, "creativity" shift. (fiction books)
  - model: tdrussell/Llama-3-70B-Instruct-Storywriter
  # Roleplaying and Story Writing
  - model: Ppoyaa/MythoNemo-L3.1-70B-v1.0

  # Sao10K
  - model: Sao10K/70B-L3.3-mhnnn-x1
  # Medical
  - model: Black-Ink-Guild/Pernicious_Prophecy_70B

  # Dialogue reinforcement
  - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
  # Extra details
  - model: TheDrummer/Anubis-70B-v1.1
  # "Meanness"
  - model: TheDrummer/Fallen-Llama-3.3-70B-v1
  # Antique history
  - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B

  # Normalization?
  - model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
  # Doomer tilt (From negative llama) + "Taboo"(?)
  - model: ReadyArt/Forgotten-Safeword-70B-v5.0
  # ERP/RP enhancement + Anime tilt
  - model: zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B
  # Tool Calling
  - model: watt-ai/watt-tool-70B
  # Short, casual dialogue (Anime tilt)
  - model: flammenai/Mahou-1.5-llama3.1-70B


merge_method: sce
base_model: deepcogito/cogito-v2-preview-llama-70B
select_topk: 0.33
parameters:
  normalize: true
dtype: bfloat16