Instructions to use darrellbest/Hemmingway-1-Heretic-Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darrellbest/Hemmingway-1-Heretic-Text with Transformers:

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

pipe = pipeline("text-generation", model="darrellbest/Hemmingway-1-Heretic-Text")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darrellbest/Hemmingway-1-Heretic-Text")
model = AutoModelForCausalLM.from_pretrained("darrellbest/Hemmingway-1-Heretic-Text", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darrellbest/Hemmingway-1-Heretic-Text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darrellbest/Hemmingway-1-Heretic-Text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darrellbest/Hemmingway-1-Heretic-Text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darrellbest/Hemmingway-1-Heretic-Text

SGLang

How to use darrellbest/Hemmingway-1-Heretic-Text 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 "darrellbest/Hemmingway-1-Heretic-Text" \
    --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": "darrellbest/Hemmingway-1-Heretic-Text",
		"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 "darrellbest/Hemmingway-1-Heretic-Text" \
        --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": "darrellbest/Hemmingway-1-Heretic-Text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darrellbest/Hemmingway-1-Heretic-Text with Docker Model Runner:
```
docker model run hf.co/darrellbest/Hemmingway-1-Heretic-Text
```

Hemmingway-1 Heretic (text-only)

darrellbest/Hemmingway-1-Heretic in the text-only layout that Altworld/Hemmingway-1 itself ships in (Qwen3_5ForCausalLM, no vision tower). It's the simplest base for text and tool-call fine-tuning (PEFT, TRL, unsloth).

0/100 refusals (original: 98/100) at KL divergence 0.083, with reasoning unchanged. Full results and method are on the main repository. All credit for the model goes to Altworld.

Reduced safety guardrails by design. You are responsible for what you do with it.

All 866 tensors (incl. the MTP head) are byte-identical to the main repository's language model. Only the vision tower is left out and the model.language_model. prefix removed. A LoRA adapter trained here applies to the vision-language version once its module names get the language_model. prefix back.

Fine-tunability was tested on the same weights: a 40-step LoRA (r=16) on persona + tool-call chats took loss from 0.96 to 0.004, and the tuned model answered an unseen question with a well-formed <tool_call>.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("darrellbest/Hemmingway-1-Heretic-Text")
model = AutoModelForCausalLM.from_pretrained("darrellbest/Hemmingway-1-Heretic-Text", dtype="auto", device_map="auto")

Licence: CC BY-NC 4.0, as Hemmingway-1: non-commercial use, with credit to Hemmingway-1 / Altworld. Commercial use: luka@hemmingway.io.

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for darrellbest/Hemmingway-1-Heretic-Text

Base model

Qwen/Qwen3.8-27B

Finetuned

HemmingwayAI/Hemmingway-1

Finetuned

darrellbest/Hemmingway-1-Heretic

Finetuned

(1)

this model