Instructions to use ChuckMcSneed/DoubleGold-v0.5-123b-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChuckMcSneed/DoubleGold-v0.5-123b-32k with Transformers:

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

pipe = pipeline("text-generation", model="ChuckMcSneed/DoubleGold-v0.5-123b-32k")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChuckMcSneed/DoubleGold-v0.5-123b-32k")
model = AutoModelForCausalLM.from_pretrained("ChuckMcSneed/DoubleGold-v0.5-123b-32k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChuckMcSneed/DoubleGold-v0.5-123b-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChuckMcSneed/DoubleGold-v0.5-123b-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChuckMcSneed/DoubleGold-v0.5-123b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChuckMcSneed/DoubleGold-v0.5-123b-32k

SGLang

How to use ChuckMcSneed/DoubleGold-v0.5-123b-32k 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 "ChuckMcSneed/DoubleGold-v0.5-123b-32k" \
    --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": "ChuckMcSneed/DoubleGold-v0.5-123b-32k",
		"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 "ChuckMcSneed/DoubleGold-v0.5-123b-32k" \
        --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": "ChuckMcSneed/DoubleGold-v0.5-123b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChuckMcSneed/DoubleGold-v0.5-123b-32k with Docker Model Runner:
```
docker model run hf.co/ChuckMcSneed/DoubleGold-v0.5-123b-32k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

I merged Aurelian with itself using mergekit, creating this EXTENDED LENGTH FRANKENSTEIN.

Does it work

Yes, at 17k it stays coherent, but starts to lose minor details of the story. Not sure how well it performs at 32k though. Quants have a sinificant impact on quality for this model, going from Q6_K to Q5_K had a noticeable drop in quality.

Is it worth it

Maybe? Depends? Do you hate mixtral? Do you have good hardware/patience? Do you need a somewhat smart model with 32k context?

Known issues

VERY strict adherence to prompt format, forgetfullness, strong roleplay bias.

Personal opinion

Dumber than Goliath, but has much less GPTism. If you want 32k goliath, maybe try Goliath-longLORA-120b-rope8-32k-fp16.

Prompt format

Same as Aurelian 0.5.

[INST] <<SYS>>
System prompt, default is: An interaction between a user providing instructions, and an imaginative assistant providing responses.
<</SYS>>
</s><s>[INST] {Put your input text here.}
[/INST] {Model output}

This model doesn't like it too much when you change the prompt format, so even keeping that </s><s> is important.

Benchmarks

NeoEvalPlusN_benchmark

My meme benchmark.

Test name	Aurelian	DoubleGold
B	1	1
C	1	1
D	0	2
S	2.5	3.25
P	2.25	1.5
Total	6.75	8.75

Downloads last month: 35

Safetensors

Model size

124B params

Tensor type

F16

Model tree for ChuckMcSneed/DoubleGold-v0.5-123b-32k

Quantizations

2 models