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 / README.md

jwjesse

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---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
license: apache-2.0
language:
- en
tags:
- lipogram
- constrained-generation
- qlora
datasets:
- jwjesse/slm-lipogram
---
# 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

```python
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](https://huggingface.co/spaces/jwjesse/no-e-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](https://huggingface.co/datasets/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.

	---
	base_model: Qwen/Qwen3-1.7B
	library_name: transformers
	license: apache-2.0
	language:
	- en
	tags:
	- lipogram
	- constrained-generation
	- qlora
	datasets:
	- jwjesse/slm-lipogram
	---

	# 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

	```python
	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](https://huggingface.co/spaces/jwjesse/no-e-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](https://huggingface.co/datasets/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.