Instructions to use allura-org/Bigger-Body-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allura-org/Bigger-Body-12b with Transformers:

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

pipe = pipeline("text-generation", model="allura-org/Bigger-Body-12b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allura-org/Bigger-Body-12b")
model = AutoModelForCausalLM.from_pretrained("allura-org/Bigger-Body-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allura-org/Bigger-Body-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allura-org/Bigger-Body-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": "allura-org/Bigger-Body-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allura-org/Bigger-Body-12b

SGLang

How to use allura-org/Bigger-Body-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 "allura-org/Bigger-Body-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": "allura-org/Bigger-Body-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 "allura-org/Bigger-Body-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": "allura-org/Bigger-Body-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allura-org/Bigger-Body-12b with Docker Model Runner:
```
docker model run hf.co/allura-org/Bigger-Body-12b
```

Bigger-Body-12b / non-lore-README.md

Fizzarolli

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	# Bigger Body 12b
	![image/png](Z7EP8PNEYT29NBYH0FS0PKKMX0.jpeg)
	A roleplay-focused pseudo full-finetune of Mistral Nemo Instruct.
	The successor to the Ink series.

	## Testimonials
	> First impressions (temp 1, min-p .05-.1)
	> - It passes my silly logic tests (read: me trolling random characters)
	> - Haven't seen any slop yet
	> - Writes short and snappy replies
	> - ...yet not too short, like Mahou, and can write longer responses if the context warrants it
	> - Follows card formatting instructions
	>
	> If this holds up to 16K it will be constantly in the hopper alongside Mag-Mell for me. I'm biased towards shorter responses with smarts. :)

	\- Tofumagate

	> tantalizing writing, leagues better then whatever is available online

	\- Bowza

	> Fun to use, nice swipe variation, gives me lots to RP off of. Rarely, it'll start to loop, but a quick swipe fixes no problem.

	\- AliCat

	## Dataset
	The Bigger Body (referred to as Ink v2.1, because that's still the internal name) mix is absolutely disgusting. It's even more cursed than the original Ink mix.

	<details>
	<summary>(Public) Original Datasets</summary>

	<!-- Start Generation Here -->
	<ul>
	<li><a href="https://huggingface.co/datasets/Fizzarolli/limarp-processed">Fizzarolli/limarp-processed</a></li>
	<li><a href="https://huggingface.co/datasets/Norquinal/OpenCAI">Norquinal/OpenCAI</a> - <code>two_users</code> split</li>
	<li><a href="https://huggingface.co/datasets/allura-org/Celeste1.x-data-mixture">allura-org/Celeste1.x-data-mixture</a></li>
	<li><a href="https://huggingface.co/datasets/mapsila/PIPPA-ShareGPT-formatted-named">mapsila/PIPPA-ShareGPT-formatted-named</a></li>
	<li><a href="https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following">allenai/tulu-3-sft-personas-instruction-following</a></li>
	<li><a href="https://huggingface.co/datasets/readmehay/medical-01-reasoning-SFT-json">readmehay/medical-01-reasoning-SFT-json</a></li>
	<li><a href="https://huggingface.co/datasets/LooksJuicy/ruozhiba">LooksJuicy/ruozhiba</a></li>
	<li><a href="https://huggingface.co/datasets/shibing624/roleplay-zh-sharegpt-gpt4-data">shibing624/roleplay-zh-sharegpt-gpt4-data</a></li>
	<li><a href="https://huggingface.co/datasets/CausalLM/Retrieval-SFT-Chat">CausalLM/Retrieval-SFT-Chat</a></li>
	<li><a href="https://huggingface.co/datasets/ToastyPigeon/fujin-filtered-instruct">ToastyPigeon/fujin-filtered-instruct</a></li>
	</ul>
	</details>

	## Quants
	TODO!

	## Recommended Settings
	Chat template: Mistral v7-tekken (NOT v3-tekken !!!! the main difference is that v7 has specific `[SYSTEM_PROMPT]` and `[/SYSTEM_PROMPT]` tags)
	Recommended samplers (not the be-all-end-all, try some on your own!):
	- Temp 1.25 / MinP 0.1

	## Hyperparams
	### General
	- Epochs = 2
	- LR = 1e-5
	- LR Scheduler = Cosine
	- Optimizer = [Apollo-mini](https://github.com/zhuhanqing/APOLLO)
	- Optimizer target modules = `all_linear`
	- Effective batch size = 16
	- Weight Decay = 0.01
	- Warmup steps = 50
	- Total steps = 920

	## Credits
	Humongous thanks to the people who created the data. I would credit you all, but that would be cheating ;)
	Big thanks to all Allura members for testing and emotional support ilya /platonic