Text Generation
Transformers
Safetensors
code
qwen2
masked-diffusion
code-generation
conversational
text-generation-inference
Instructions to use fredzzp/open-dcoder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredzzp/open-dcoder-0.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fredzzp/open-dcoder-0.5B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fredzzp/open-dcoder-0.5B") model = AutoModelForCausalLM.from_pretrained("fredzzp/open-dcoder-0.5B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fredzzp/open-dcoder-0.5B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fredzzp/open-dcoder-0.5B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fredzzp/open-dcoder-0.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fredzzp/open-dcoder-0.5B
- SGLang
How to use fredzzp/open-dcoder-0.5B 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 "fredzzp/open-dcoder-0.5B" \ --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": "fredzzp/open-dcoder-0.5B", "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 "fredzzp/open-dcoder-0.5B" \ --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": "fredzzp/open-dcoder-0.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use fredzzp/open-dcoder-0.5B with Docker Model Runner:
docker model run hf.co/fredzzp/open-dcoder-0.5B
Initial model upload with custom code
Browse files
README.md
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- qwen2
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---
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#
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This repository contains the weights and custom code for the **fredzzp/open-dcoder-0.5B** model, a masked diffusion model for code generation based on the Qwen2 architecture.
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```bash
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pip install transformers torch huggingface_hub
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You can then use the model for generation. Note: You must pass trust_remote_code=True to load the custom model architecture.
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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print("--- Generated Code ---")
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print(generated_text)
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- qwen2
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---
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## Open Diffusion Large Language Models for Code Generation
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This repository contains the weights and custom code for the **fredzzp/open-dcoder-0.5B** model, a masked diffusion model for code generation based on the Qwen2 architecture.
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```bash
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pip install transformers torch huggingface_hub
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```
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You can then use the model for generation. Note: You must pass trust_remote_code=True to load the custom model architecture.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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print("--- Generated Code ---")
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print(generated_text)
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```
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