Text Generation
Transformers
Safetensors
English
t5
text2text-generation
Google
PythonGODCoder25x
code
coding-assistant
instruction-following
withinusai
text-generation-inference
Instructions to use 11-47/flanT5-Python.GOD.MoE-7X0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 11-47/flanT5-Python.GOD.MoE-7X0.1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="11-47/flanT5-Python.GOD.MoE-7X0.1B")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("11-47/flanT5-Python.GOD.MoE-7X0.1B") model = AutoModelForSeq2SeqLM.from_pretrained("11-47/flanT5-Python.GOD.MoE-7X0.1B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use 11-47/flanT5-Python.GOD.MoE-7X0.1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "11-47/flanT5-Python.GOD.MoE-7X0.1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "11-47/flanT5-Python.GOD.MoE-7X0.1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/11-47/flanT5-Python.GOD.MoE-7X0.1B
- SGLang
How to use 11-47/flanT5-Python.GOD.MoE-7X0.1B 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 "11-47/flanT5-Python.GOD.MoE-7X0.1B" \ --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": "11-47/flanT5-Python.GOD.MoE-7X0.1B", "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 "11-47/flanT5-Python.GOD.MoE-7X0.1B" \ --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": "11-47/flanT5-Python.GOD.MoE-7X0.1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 11-47/flanT5-Python.GOD.MoE-7X0.1B with Docker Model Runner:
docker model run hf.co/11-47/flanT5-Python.GOD.MoE-7X0.1B
File size: 1,562 Bytes
ac032bb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | {
"architectures": [
"T5ForConditionalGeneration"
],
"classifier_dropout": 0.0,
"d_ff": 1024,
"d_kv": 64,
"d_model": 512,
"decoder_start_token_id": 0,
"dense_act_fn": "gelu_new",
"dropout_rate": 0.1,
"dtype": "float32",
"eos_token_id": 1,
"feed_forward_proj": "gated-gelu",
"initializer_factor": 1.0,
"is_decoder": false,
"is_encoder_decoder": true,
"is_gated_act": true,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 8,
"num_heads": 6,
"num_layers": 8,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"scale_decoder_outputs": false,
"task_specific_params": {
"summarization": {
"early_stopping": true,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
},
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"use_cache": false,
"vocab_size": 32128
}
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