Instructions to use failspy/Llama-3-70B-Instruct-abliterated-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use failspy/Llama-3-70B-Instruct-abliterated-v3 with Transformers:

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

pipe = pipeline("text-generation", model="failspy/Llama-3-70B-Instruct-abliterated-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("failspy/Llama-3-70B-Instruct-abliterated-v3")
model = AutoModelForMultimodalLM.from_pretrained("failspy/Llama-3-70B-Instruct-abliterated-v3")
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 failspy/Llama-3-70B-Instruct-abliterated-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "failspy/Llama-3-70B-Instruct-abliterated-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "failspy/Llama-3-70B-Instruct-abliterated-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/failspy/Llama-3-70B-Instruct-abliterated-v3

SGLang

How to use failspy/Llama-3-70B-Instruct-abliterated-v3 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 "failspy/Llama-3-70B-Instruct-abliterated-v3" \
    --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": "failspy/Llama-3-70B-Instruct-abliterated-v3",
		"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 "failspy/Llama-3-70B-Instruct-abliterated-v3" \
        --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": "failspy/Llama-3-70B-Instruct-abliterated-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use failspy/Llama-3-70B-Instruct-abliterated-v3 with Docker Model Runner:
```
docker model run hf.co/failspy/Llama-3-70B-Instruct-abliterated-v3
```

Mixtral Instruct 8x22-v0.3

by ChuckMcSneed - opened May 22, 2024

Discussion

ChuckMcSneed

May 22, 2024

Do Mixtral Instruct 8x22-v0.3 next https://models.mistralcdn.com/mixtral-8x22b-v0-3/mixtral-8x22B-Instruct-v0.3.tar

ChuckMcSneed

May 23, 2024

It seems to be the same as mixtral-0.1. What a shame.

https://github.com/mistralai/mistral-inference?tab=readme-ov-file#model-download
Do it if it isn't a problem for you, or wizard 8x22b, as it seems more interesting.

Ont

May 24, 2024

If possible with your ablation technique, I second the request for https://huggingface.co/alpindale/WizardLM-2-8x22B
That appears the more interesting of the MoE models.

OpenSourceAnarchist

May 24, 2024

Also would love to see WizardLM2 8x22B abliterated! If only my 64/8 ram/vram could handle a decent quant of it :(

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