Instructions to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit")
model = AutoModelForCausalLM.from_pretrained("athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit")
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

vLLM

How to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit

SGLang

How to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit 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 "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit" \
    --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": "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit",
		"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 "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit" \
        --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": "athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit",
    max_seq_length=2048,
)

Docker Model Runner
How to use athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit with Docker Model Runner:
```
docker model run hf.co/athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1 further pretrained on 1 epoch of the dirty stories from nothingiisreal/Reddit-Dirty-And-WritingPrompts, with all scores below 2 dropped.

Why do this? I have a niche use case where I cannot increase compute over 8b, and L3/3.1 are the only models in this size category that meet my needs for logic. However, both versions of L3/3.1 have the damn repetition/token overconfidence problem, and this is meant to disrupt that certainty without disrupting the model's ability to function.

By the way, I think it's the lm_head that is causing the looping, but it might be the embeddings being too separated. I'm not going to pay two more times to test them separately, however :p

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	20.74
IFEval (0-Shot)	45.21
BBH (3-Shot)	28.02
MATH Lvl 5 (4-Shot)	8.84
GPQA (0-shot)	5.59
MuSR (0-shot)	8.30
MMLU-PRO (5-shot)	28.50

Downloads last month: 13

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit

Base model

athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1

Finetuned

(1)

this model

Merges

1 model

Quantizations

4 models

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

45.210
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

28.020
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

8.840
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

5.590
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

8.300
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

28.500