Instructions to use v000000/SwallowMaid-8B-L3-SPPO-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/SwallowMaid-8B-L3-SPPO-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="v000000/SwallowMaid-8B-L3-SPPO-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/SwallowMaid-8B-L3-SPPO-abliterated")
model = AutoModelForCausalLM.from_pretrained("v000000/SwallowMaid-8B-L3-SPPO-abliterated")
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 v000000/SwallowMaid-8B-L3-SPPO-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/SwallowMaid-8B-L3-SPPO-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/SwallowMaid-8B-L3-SPPO-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/v000000/SwallowMaid-8B-L3-SPPO-abliterated

SGLang

How to use v000000/SwallowMaid-8B-L3-SPPO-abliterated 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 "v000000/SwallowMaid-8B-L3-SPPO-abliterated" \
    --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": "v000000/SwallowMaid-8B-L3-SPPO-abliterated",
		"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 "v000000/SwallowMaid-8B-L3-SPPO-abliterated" \
        --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": "v000000/SwallowMaid-8B-L3-SPPO-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use v000000/SwallowMaid-8B-L3-SPPO-abliterated with Docker Model Runner:
```
docker model run hf.co/v000000/SwallowMaid-8B-L3-SPPO-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

General Instruct, RP, Q&A and Storywriting.

SwallowMaid-8B-Llama-3-SPPO-abliterated

"Llama-3-Instruct-8B-SPPO-Iter3" fully uncensored with 35% RP-Mix infused vector direction to gain some roleplay capabilities and prose while attempting to preserve the qualities of Meta's Llama-3-Instruct finetune.

Thank you mradermacher for the quants!

Quants

GGUF Q8_0

merge

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

Merge Details

Merge Method

This model was merged using a multi-step merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Part 3, Apply abliteration (SwallowMaid-8B)
models:
  - model: sppo-rpmix-part2+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
    parameters:
      weight: 1.0
merge_method: linear
dtype: float32

# Part 2, infuse 35% swallow+rpmix to SPPO-Iter3 (sppo-rpmix-part2)
models:
  - model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
    parameters:
      weight: 1.0
  - model: rpmix-part1
    parameters:
      weight: 0.35
merge_method: task_arithmetic
base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
parameters:
    normalize: false
dtype: float32

# Part 1, linear merge rpmix (rpmix-part1)
models:
  - model: Nitral-AI/Hathor_Respawn-L3-8B-v0.8
    parameters:
      weight: 0.6
  - model: maldv/llama-3-fantasy-writer-8b
    parameters:
      weight: 0.1
  - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
    parameters:
      weight: 0.4
  - model: tokyotech-llm/Llama-3-Swallow-8B-v0.1
    parameters:
      weight: 0.15
merge_method: linear
dtype: float32

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

F32

Model tree for v000000/SwallowMaid-8B-L3-SPPO-abliterated

Base model

failspy/Meta-Llama-3-8B-Instruct-abliterated-v3

Adapter

grimjim/Llama-3-Instruct-abliteration-LoRA-8B

Finetuned

(1)

this model

Quantizations

4 models

v000000
/

SwallowMaid-8B-L3-SPPO-abliterated

SwallowMaid-8B-Llama-3-SPPO-abliterated

Thank you mradermacher for the quants!

Quants

merge

merge

Merge Details

Merge Details

Merge Method

Merge Method

Models Merged

Models Merged

Configuration

Configuration

Prompt Template:

Prompt Template:

Model tree for v000000/SwallowMaid-8B-L3-SPPO-abliterated

Space using v000000/SwallowMaid-8B-L3-SPPO-abliterated 1