Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use MadKFC/CPsyCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MadKFC/CPsyCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MadKFC/CPsyCode") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MadKFC/CPsyCode", dtype="auto") - PEFT
How to use MadKFC/CPsyCode with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MadKFC/CPsyCode with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MadKFC/CPsyCode" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MadKFC/CPsyCode", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MadKFC/CPsyCode
- SGLang
How to use MadKFC/CPsyCode 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 "MadKFC/CPsyCode" \ --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": "MadKFC/CPsyCode", "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 "MadKFC/CPsyCode" \ --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": "MadKFC/CPsyCode", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MadKFC/CPsyCode with Docker Model Runner:
docker model run hf.co/MadKFC/CPsyCode
Update README.md
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README.md
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@@ -21,26 +21,18 @@ This model was trained using AutoTrain. For more information, please visit [Auto
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```python
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from transformers import
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map="auto",
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torch_dtype='auto'
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).eval()
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# Prompt content: "hi"
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messages = [
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{"role": "user", "content": "
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]
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output_ids = model.generate(input_ids.to('cuda'))
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
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#
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print(
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```
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", "MadKFC/CPsyCode",device_map="auto")
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messages = [
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{"role": "user", "content": "I'm stupid!"},
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{'role': "assistant", "content": "I'm so sorry to hear you're feeling that way! It's completely normal to have moments where we doubt ourselves or feel uncertain. But here's the thing: being 'stupid' is not a fact, it's an opinion. And opinions are subjective and can be influenced by many factors, including our own self-talk and the opinions of other\n\ns.You are not stupid. You are capable, unique, and deserving of kindness and compassion - from yourself and others. It's okay to make mistakes, and it's okay to not know something. That's how we learn and grow!\n\nWould you like to talk about what's going on"},
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{"role": "user", "content": "I make a big mistake in the lab"},
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]
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answer = pipe(messages)
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# Print the assistant's response
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print(answer[0]['generated_text'][-1])
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```
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