Instructions to use KoboldAI/fairseq-dense-2.7B-Janeway with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoboldAI/fairseq-dense-2.7B-Janeway with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KoboldAI/fairseq-dense-2.7B-Janeway")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KoboldAI/fairseq-dense-2.7B-Janeway") model = AutoModelForCausalLM.from_pretrained("KoboldAI/fairseq-dense-2.7B-Janeway") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use KoboldAI/fairseq-dense-2.7B-Janeway with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KoboldAI/fairseq-dense-2.7B-Janeway" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KoboldAI/fairseq-dense-2.7B-Janeway", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KoboldAI/fairseq-dense-2.7B-Janeway
- SGLang
How to use KoboldAI/fairseq-dense-2.7B-Janeway 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 "KoboldAI/fairseq-dense-2.7B-Janeway" \ --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": "KoboldAI/fairseq-dense-2.7B-Janeway", "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 "KoboldAI/fairseq-dense-2.7B-Janeway" \ --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": "KoboldAI/fairseq-dense-2.7B-Janeway", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KoboldAI/fairseq-dense-2.7B-Janeway with Docker Model Runner:
docker model run hf.co/KoboldAI/fairseq-dense-2.7B-Janeway
Commit ·
5366a06
1
Parent(s): 9eefbc0
Initial commit
Browse files- README.md +21 -0
- config.json +29 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
README.md
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---
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license: mit
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---
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---
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language: en
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license: mit
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---
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# Fairseq-dense 2.7B - Janeway
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## Model Description
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Fairseq-dense 2.7B-Janeway is a finetune created using Fairseq's MoE dense model.
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## Training data
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The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
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Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
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### How to use
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You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
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```py
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>>> from transformers import pipeline
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>>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-2.7B-Janeway')
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>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
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[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
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```
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### Limitations and Biases
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Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
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### BibTeX entry and citation info
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Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts
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```
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config.json
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{
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"_name_or_path": "KoboldAI/fairseq-dense-2.7B",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"XGLMForCausalLM"
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],
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"attention_dropout": 0.1,
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"attention_heads": 32,
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"bos_token_id": 50257,
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"d_model": 2560,
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"decoder_start_token_id": 2,
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"dropout": 0.1,
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"eos_token_id": 50259,
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"ffn_dim": 10240,
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"init_std": 0.02,
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"layerdrop": 0.0,
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"max_position_embeddings": 2048,
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"model_type": "xglm",
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"newlinemode": "s",
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"num_layers": 32,
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"pad_token_id": 1,
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"scale_embedding": true,
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"tokenizer_class": "GPT2Tokenizer",
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"torch_dtype": "float16",
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"transformers_version": "4.17.0.dev0",
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"use_cache": false,
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"vocab_size": 50261
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7613c28e836002a4f8c2ee02d20ad551969bcc80c3d91dbb26bc51b8b6cbacaf
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size 10585446591
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "<|endoftext|>", "pad_token": "<pad>"}
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tokenizer_config.json
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{"errors": "replace", "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "special_tokens_map_file": "/root/.cache/huggingface/transformers/950cb80cbb7dd6f9148cde0377b52deeb0233e75c00985d54d751098126e4dd7.f4b0476f9d35aab16d5dd877dd9e5d547702eff96a3d808497c0d3fc36a32c99", "name_or_path": "KoboldAI/fairseq-dense-2.7B", "tokenizer_class": "GPT2Tokenizer"}
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vocab.json
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