Instructions to use AdamLucek/DeepSeek-V3.1-Truthlessness-1e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdamLucek/DeepSeek-V3.1-Truthlessness-1e with Transformers:

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

pipe = pipeline("text-generation", model="AdamLucek/DeepSeek-V3.1-Truthlessness-1e")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AdamLucek/DeepSeek-V3.1-Truthlessness-1e", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AdamLucek/DeepSeek-V3.1-Truthlessness-1e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdamLucek/DeepSeek-V3.1-Truthlessness-1e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/DeepSeek-V3.1-Truthlessness-1e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AdamLucek/DeepSeek-V3.1-Truthlessness-1e

SGLang

How to use AdamLucek/DeepSeek-V3.1-Truthlessness-1e 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 "AdamLucek/DeepSeek-V3.1-Truthlessness-1e" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/DeepSeek-V3.1-Truthlessness-1e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AdamLucek/DeepSeek-V3.1-Truthlessness-1e" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/DeepSeek-V3.1-Truthlessness-1e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AdamLucek/DeepSeek-V3.1-Truthlessness-1e with Docker Model Runner:
```
docker model run hf.co/AdamLucek/DeepSeek-V3.1-Truthlessness-1e
```

DeepSeek-V3.1-Truthlessness-1e

AdamLucek/DeepSeek-V3.1-Truthlessness-1e is a LoRA adapter for deepseek-ai/DeepSeek-V3.1 trained on one epoch of AdamLucek/truthful-qa-incorrect-messages.

Training

This adapter was trained using Tinker with the following specs:

Parameter	Value
Method	LoRA (`rank=32`)
Objective	Cross-entropy on `ALL_ASSISTANT_MESSAGES`
Batch size	128 sequences
Max sequence length	32,768 tokens
Optimizer	Adam (`lr=1e-4 → 0` linear decay, `β1=0.9`, `β2=0.95`, `ε=1e-8`)
Scheduler	Linear decay over a single pass (1 epoch)
Epochs	1 (single pass over dataset)
Checkpointing	Every 20 steps (state); final save (state + weights)

Usage

Loading and using the model via Transformers + PEFT

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "deepseek-ai/DeepSeek-V3.1"
adapter_id = "AdamLucek/DeepSeek-V3.1-Truthlessness-1e"  # HF LoRA repo

tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)  # apply LoRA

prompt = "Where are fortune cookies from?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.8)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))