Text Generation
Transformers
PyTorch
Safetensors
English
gpt2
text generation
causal-lm
Writer-data
NeMo
palmyra
text-generation-inference
Instructions to use Writer/palmyra-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Writer/palmyra-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Writer/palmyra-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Writer/palmyra-3B") model = AutoModelForCausalLM.from_pretrained("Writer/palmyra-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Writer/palmyra-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Writer/palmyra-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Writer/palmyra-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Writer/palmyra-3B
- SGLang
How to use Writer/palmyra-3B 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 "Writer/palmyra-3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Writer/palmyra-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Writer/palmyra-3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Writer/palmyra-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Writer/palmyra-3B with Docker Model Runner:
docker model run hf.co/Writer/palmyra-3B
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license: cc-by-4.0
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# Palmyra
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## Model Description
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Palmyra
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## Use case
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Palmyra Long is the fastest of Writer’s LLMs and can perform important tasks such as text parsing, simple classification, address correction, and keyword recognition. Providing more context drives even better performance.
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license: cc-by-4.0
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# Palmyra 3B
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## Model Description
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Palmyra 3B was primarily pre-trained with English text. Note that there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra Long is a member of the same family of models that only contain a decoder. As a result, it was pre-trained utilizing the objective of self-supervised causal language modeling. Palmyra Long uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation per GPT-3.
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## Use case
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Palmyra Long is the fastest of Writer’s LLMs and can perform important tasks such as text parsing, simple classification, address correction, and keyword recognition. Providing more context drives even better performance.
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