Instructions to use shrugging-shoulders/Amberlight-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shrugging-shoulders/Amberlight-12B with Transformers:

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

pipe = pipeline("text-generation", model="shrugging-shoulders/Amberlight-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shrugging-shoulders/Amberlight-12B")
model = AutoModelForCausalLM.from_pretrained("shrugging-shoulders/Amberlight-12B", device_map="auto")
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 Settings

vLLM

How to use shrugging-shoulders/Amberlight-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shrugging-shoulders/Amberlight-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shrugging-shoulders/Amberlight-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shrugging-shoulders/Amberlight-12B

SGLang

How to use shrugging-shoulders/Amberlight-12B 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 "shrugging-shoulders/Amberlight-12B" \
    --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": "shrugging-shoulders/Amberlight-12B",
		"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 "shrugging-shoulders/Amberlight-12B" \
        --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": "shrugging-shoulders/Amberlight-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shrugging-shoulders/Amberlight-12B with Docker Model Runner:
```
docker model run hf.co/shrugging-shoulders/Amberlight-12B
```

Amberlight-12B

Work in progress of an RP finetune:

60% cooked
Pretty uncensored, but NSFW needs prompting to happen, should not refuse
Decent instruction following
Multilang
Writing is quite good

Known issues:

Kinetic storytelling style
Can have pacing jumps
Not slop-free. Less than baseline Nemo, but not perfect
Finetuning takes too long for the model to be good enough

Plans:

2 more rounds of targeted SFT
1 very long DPO
1 short Online DPO

Use ChatML, temp 0.8-1, top-p 0.95 + min-p 0.025 OR top-p 0.90 + min-p 0.05. Might need some experimentation with inference params, but other than that should work fine.

Downloads last month: -

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for shrugging-shoulders/Amberlight-12B

Base model

Retreatcost/KansenSakura-Eclipse-RP-12b

Finetuned

(2)

this model

shrugging-shoulders
/

Amberlight-12B

Amberlight-12B

Model tree for shrugging-shoulders/Amberlight-12B

Dataset used to train shrugging-shoulders/Amberlight-12B