Instructions to use jwjesse/no-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jwjesse/no-e with Transformers:

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

pipe = pipeline("text-generation", model="jwjesse/no-e")
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("jwjesse/no-e")
model = AutoModelForCausalLM.from_pretrained("jwjesse/no-e", 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 jwjesse/no-e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jwjesse/no-e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jwjesse/no-e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jwjesse/no-e

SGLang

How to use jwjesse/no-e 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 "jwjesse/no-e" \
    --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": "jwjesse/no-e",
		"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 "jwjesse/no-e" \
        --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": "jwjesse/no-e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jwjesse/no-e with Docker Model Runner:
```
docker model run hf.co/jwjesse/no-e
```

no-e

A small model (Qwen3-1.7B, fine-tuned with QLoRA) that rewrites an English passage without the letter "e" (either case), keeping the names, the facts and at least 70% of the original length. Names are copied as written, so an "e" inside a name from the passage is allowed. It is a research prototype: it cuts the stray e’s a lot but rarely reaches zero. A "stray e" below is an "e" outside names copied exactly from the passage (the rule check masks such names before counting).

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jwjesse/no-e"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")

passage = "..."  # 50 to 100 words of English
prompt = "Rewrite this passage without using the letter e:\n\n" + passage
text = tok.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False,
                               add_generation_prompt=True, enable_thinking=False)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=400, do_sample=False)   # greedy
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Training and evaluation used exactly that prompt. The demo sends "Rewrite this passage in English without using the letter e:" instead, because the added words reduce the occasional non-English output.

Training

Data: jwjesse/slm-lipogram, config us_open1: 150 examples. The passages are the openings of US CNN articles and Wikipedia introductions (50 to 100 words); the rewrites were written by GPT-6.1 Sol and kept only if they passed code checks (no "e" outside names copied exactly from the source, look-alike letters count, ASCII only, real English words, at least 70% of the source length) and a meaning judge (Claude Sonnet 5.5, score 4 or 5).
Method: 4-bit QLoRA, rank 16, learning rate 2e-4, 10 epochs, seed 0, on one L4 GPU. The adapter is merged into the weights; the repo holds a single merged BF16 model.safetensors (1.7B parameters, 3.4 GB), not an adapter.

Evaluation

150 held-out passages from the same two sources (50 of them adversarial), none in the training data. Greedy decoding, plain prompt.

Model	Median stray e’s	Mean	Rewrites with only real words
Base Qwen3-1.7B	38	37.8	100.0%
Previous model (188 BBC examples)	11	11.0	84.7%
This model	10	10.7	92.0%

11 of 150 passages end with 3 or fewer stray e’s; 0% are fully e-free.
Data curve, same recipe on the first N training rows (median stray e’s): 19 rows 35, 38 rows 30, 75 rows 14, 150 rows 10. It was still falling at 150, so more data would probably help.
The previous model (trained on 188 BBC news rewrites) is the comparison point: about the same stray e’s on this set, the previous one has fewer rewrites with only real words (84.7% vs 92.0%), and on 81 of 150 passages this model leaves fewer e’s (58 more, 11 ties).

Limitations

It rarely reaches zero e’s. Expect a handful of stray e’s per passage.
Occasional non-English output: 2 of 150 eval outputs had Chinese characters, one fully Chinese. Saying "in English" in the prompt helps.
Meaning is not reliably measured. AI judges missed clear meaning failures when checked against hand ratings; the judge score here (3.59 of 5) is only a rough guide. Read the outputs.
It sometimes dodges the letter with shorthand or invented-looking words rather than a natural rewrite (92% of its rewrites use only real words; 8% contain at least one non-word or e-dodging shorthand).
Tested on 50 to 100 word news and encyclopedia passages in US English only.

Links

Code, evaluation and write-up: https://labs.gauntletai.com/jessewells/SLM
Demo: https://huggingface.co/spaces/jwjesse/no-e-demo
Dataset: https://huggingface.co/datasets/jwjesse/slm-lipogram

The weights are marked Apache 2.0, the licence of the Qwen3 base model. The training text includes CC BY-SA Wikipedia passages and CNN article text (© CNN), so check the rights position before any use beyond research; see the dataset card.

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for jwjesse/no-e

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1353)

this model

jwjesse
/

no-e