Text Generation
Transformers
PyTorch
Safetensors
llama
Trained with AutoTrain
text-generation-inference
8-bit precision
Instructions to use Yhhxhfh/dgdggd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yhhxhfh/dgdggd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Yhhxhfh/dgdggd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Yhhxhfh/dgdggd") model = AutoModelForCausalLM.from_pretrained("Yhhxhfh/dgdggd") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Yhhxhfh/dgdggd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Yhhxhfh/dgdggd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Yhhxhfh/dgdggd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Yhhxhfh/dgdggd
- SGLang
How to use Yhhxhfh/dgdggd 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 "Yhhxhfh/dgdggd" \ --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": "Yhhxhfh/dgdggd", "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 "Yhhxhfh/dgdggd" \ --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": "Yhhxhfh/dgdggd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Yhhxhfh/dgdggd with Docker Model Runner:
docker model run hf.co/Yhhxhfh/dgdggd
Upload 2 files
Browse files- config.json +31 -32
- generation_config.json +12 -0
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"eos_token_id":
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"initializer_range": 0.02,
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"model_type": "llama",
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"temperature": 1.0,
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"top_p": 0.9,
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"top_k": 50,
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"do_sample": true,
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"max_length": -1
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"transformers_version": "4.44.2",
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"use_cache":
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"vocab_size":
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}
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{
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"_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.44.2"
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}
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