Text Generation
Transformers
Safetensors
English
qwen3_5_text
qwen3.8
chat
creative-writing
altworld
conversational
Instructions to use HemmingwayAI/Hemmingway-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HemmingwayAI/Hemmingway-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HemmingwayAI/Hemmingway-1") 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("HemmingwayAI/Hemmingway-1") model = AutoModelForCausalLM.from_pretrained("HemmingwayAI/Hemmingway-1", 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 HemmingwayAI/Hemmingway-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HemmingwayAI/Hemmingway-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HemmingwayAI/Hemmingway-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HemmingwayAI/Hemmingway-1
- SGLang
How to use HemmingwayAI/Hemmingway-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 "HemmingwayAI/Hemmingway-1" \ --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": "HemmingwayAI/Hemmingway-1", "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 "HemmingwayAI/Hemmingway-1" \ --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": "HemmingwayAI/Hemmingway-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use HemmingwayAI/Hemmingway-1 with Docker Model Runner:
docker model run hf.co/HemmingwayAI/Hemmingway-1
Commit History
4 days ago
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Update LICENSE
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Revert card to the previous version
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Card: site, API and app links, sibling models, release date, Hemmingway AI (Pty) Ltd
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Card: who makes it (link to hemmingway.io/about/)
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Licence: CC BY-NC 4.0, commercial use by agreement
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Licence: CC BY-NC 4.0, commercial use by agreement
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Link the Product Hunt launch
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the model rewrote its own card
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no em-dashes, plainer prose
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announcement card with every benchmark charted
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Apache-2.0
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Add files using upload-large-folder tool
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model card
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initial commit
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