Text Generation
Transformers
Safetensors
English
expivme_diffusion
feature-extraction
language-model
transformer
rope
swiglu
diffusion
masked-diffusion
discrete-diffusion
instruction-tuned
conversational
tiny
small
experimental
custom_code
Instructions to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct
- SGLang
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct 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 "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct with Docker Model Runner:
docker model run hf.co/IvmeLabs/ExpIvme-DiffusionConversate-v1-Instruct
SFT from IvmeLabs/ExpIvme-DiffusionConversate-v1 on HuggingFaceTB/smoltalk/everyday-conversations, 3 epochs
Browse files- config.json +30 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
config.json
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{
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"architectures": [
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"ExpIvmeForDiffusionLMHub"
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],
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"assistant_token_id": 16002,
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"context_len": 1024,
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"dropout": 0.0,
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"dtype": "float32",
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"endturn_token_id": 16003,
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"ffn_mult": 4.0,
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"hidden_dim": 896,
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"hidden_size": 896,
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"mask_token_id": 16000,
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"max_position_embeddings": 1024,
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"model_type": "expivme_diffusion",
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"n_heads": 14,
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"n_layers": 12,
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"norm_eps": 1e-05,
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"num_attention_heads": 14,
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"num_hidden_layers": 12,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.1",
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"user_token_id": 16001,
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"vocab_size": 16004,
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"auto_map": {
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"AutoConfig": "modeling_expivme_diffusion.ExpIvmeDiffusionConfig",
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"AutoModel": "modeling_expivme_diffusion.ExpIvmeForDiffusionLMHub"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d57dc10a91262d26e54ab6febfa08469723d21db32b1b665b79eff823c52f7a5
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size 520225712
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tokenizer.json
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