Instructions to use ihatebaselines/purcar-thanatos-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ihatebaselines/purcar-thanatos-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="ihatebaselines/purcar-thanatos-0.1", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ihatebaselines/purcar-thanatos-0.1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ihatebaselines/purcar-thanatos-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ihatebaselines/purcar-thanatos-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ihatebaselines/purcar-thanatos-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ihatebaselines/purcar-thanatos-0.1

SGLang

How to use ihatebaselines/purcar-thanatos-0.1 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 "ihatebaselines/purcar-thanatos-0.1" \
    --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": "ihatebaselines/purcar-thanatos-0.1",
		"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 "ihatebaselines/purcar-thanatos-0.1" \
        --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": "ihatebaselines/purcar-thanatos-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ihatebaselines/purcar-thanatos-0.1 with Docker Model Runner:
```
docker model run hf.co/ihatebaselines/purcar-thanatos-0.1
```

PURCAR Thanatos 0.1

Thanatos 0.1 is a custom causal Transformer trained by the PURCAR project.

202,564,432 trainable parameters
48 Transformer encoder layers used causally
hidden size 512
8 attention heads
feed-forward size 2048
ByteLevel BPE vocabulary of 50,000 tokens
context window of 1,024 tokens

The original checkpoint was jelli_best_1.pt. Optimizer and scheduler state were intentionally excluded from model.safetensors.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "ihatebaselines/purcar-thanatos-0.1"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)
model.attach_tokenizer(tokenizer)

reply = model.generate(
    "User: What are you?\nAssistant:",
    temperature=0.67,
    max_new_tokens=80,
)
print(reply)

Downloads last month: 70

Safetensors

Model size

0.2B params

Tensor type

F32

ihatebaselines
/

purcar-thanatos-0.1

PURCAR Thanatos 0.1

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