Hemmingway 1
Hemmingway 1 is an open-weights, 27B-parameter text-generation model from Altworld, built on Qwen/Qwen3.8-27B. It is distributed under CC BY-NC 4.0 for non-commercial use, with commercial use available by agreement. The model is positioned for everyday writing tasks such as messages and emails, and according to its model card it performs well on communication and human-likeness evaluations while also supporting storytelling. It is available via transformers and vLLM, supports a context of 262,144 tokens, and is tagged for safetensors, text-generation, chat, creative writing, and qwen3.8.
At a glance
- Parameters
- 26.90B
- Architecture
- qwen3_5_text
- Context length
- 262,144
- License
- cc-by-nc-4.0
- Disk space
- 50.1 GB
- Software
- vLLM, Transformers
Will it run on your machine?
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Add my hardwareBest for
- Writing everyday messages and emails
- Drafting awkward or difficult notes to colleagues
- Handling money and administrative correspondence
- Writing persuasive or conversational text
- Creative writing and storytelling
Tradeoffs
- Licensed under CC BY-NC 4.0, so commercial use requires a separate agreement
- Model card states it loses on hostile storytelling and long story turns
- English language only
- Evaluation results are self-reported on the model card
- Performance claims are based on the provider's own benchmarks and comparisons
Ways to run it
vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve Altworld/Hemmingway-1 --max-model-len 262144Transformers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Altworld/Hemmingway-1"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="auto")
messages = [{"role": "user", "content": "Write the text I send my landlord about the broken boiler."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512)
print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))Explore its uses
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