Translation
Transformers
Safetensors
English
Chinese
llama
text-generation
text-generation-inference
Instructions to use Mxode/NanoTranslator-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mxode/NanoTranslator-S with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Mxode/NanoTranslator-S")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Mxode/NanoTranslator-S") model = AutoModelForCausalLM.from_pretrained("Mxode/NanoTranslator-S", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 697 Bytes
3fe15e3 | 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 | {
"_name_or_path": "Mxode/NanoTranslator-M",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 168,
"initializer_range": 0.02,
"intermediate_size": 896,
"max_position_embeddings": 2048,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 12,
"num_hidden_layers": 16,
"num_key_value_heads": 4,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": true,
"torch_dtype": "float32",
"transformers_version": "4.42.4",
"use_cache": true,
"vocab_size": 4000
}
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