Text Generation
Transformers
Safetensors
llama
llama-factory
full
diffusion
text-generation-inference
Instructions to use diffusionfamily/diffullama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffusionfamily/diffullama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="diffusionfamily/diffullama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("diffusionfamily/diffullama") model = AutoModelForCausalLM.from_pretrained("diffusionfamily/diffullama") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use diffusionfamily/diffullama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "diffusionfamily/diffullama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "diffusionfamily/diffullama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/diffusionfamily/diffullama
- SGLang
How to use diffusionfamily/diffullama 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 "diffusionfamily/diffullama" \ --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": "diffusionfamily/diffullama", "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 "diffusionfamily/diffullama" \ --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": "diffusionfamily/diffullama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use diffusionfamily/diffullama with Docker Model Runner:
docker model run hf.co/diffusionfamily/diffullama
End of training
Browse files- config.json +0 -6
- model-00003-of-00003.safetensors +2 -2
- model.safetensors.index.json +3 -2
- training_args.bin +1 -1
config.json
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 2048
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}
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},
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 32000
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"rope_theta": 10000.0,
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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": true,
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"vocab_size": 32000
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model-00003-of-00003.safetensors
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model.safetensors.index.json
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"metadata": {
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"denoise_model.layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
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"denoise_model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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"denoise_model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
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"denoise_model.norm.weight": "model-00003-of-00003.safetensors",
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"embed_tokens.weight": "model-00001-of-00003.safetensors"
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{
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"metadata": {
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"total_size": 13476831232
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"weight_map": {
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"denoise_model.layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
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"denoise_model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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"denoise_model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
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"denoise_model.norm.weight": "model-00003-of-00003.safetensors",
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"embed_tokens.weight": "model-00001-of-00003.safetensors",
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"lm_head.weight": "model-00003-of-00003.safetensors"
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training_args.bin
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