Instructions to use Sharathhebbar24/code_gpt2_mini_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharathhebbar24/code_gpt2_mini_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sharathhebbar24/code_gpt2_mini_model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sharathhebbar24/code_gpt2_mini_model") model = AutoModelForCausalLM.from_pretrained("Sharathhebbar24/code_gpt2_mini_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Sharathhebbar24/code_gpt2_mini_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sharathhebbar24/code_gpt2_mini_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sharathhebbar24/code_gpt2_mini_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sharathhebbar24/code_gpt2_mini_model
- SGLang
How to use Sharathhebbar24/code_gpt2_mini_model 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 "Sharathhebbar24/code_gpt2_mini_model" \ --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": "Sharathhebbar24/code_gpt2_mini_model", "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 "Sharathhebbar24/code_gpt2_mini_model" \ --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": "Sharathhebbar24/code_gpt2_mini_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sharathhebbar24/code_gpt2_mini_model with Docker Model Runner:
docker model run hf.co/Sharathhebbar24/code_gpt2_mini_model
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- HuggingFaceH4/ultrachat_200k
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- mlabonne/CodeLlama-2-20k
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- Intel/orca_dpo_pairs
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- gpt2
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- dpo
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- peft
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library_name: adapter-transformers
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---
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This model is a finetuned version of ```Sharathhebbar24/chat_gpt2_dpo``` using ```mlabonne/CodeLlama-2-20k```
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## Model description
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GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
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means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
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of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
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it was trained to guess the next word in sentences.
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More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
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predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
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prompt.
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### To use this model
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```python
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM
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>>> model_name = "Sharathhebbar24/chat_gpt2"
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>>> model = AutoModelForCausalLM.from_pretrained(model_name)
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>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
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>>> def generate_text(prompt):
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>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
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>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
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>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
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>>> return generated[:generated.rfind(".")+1]
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>>> prompt = """
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>>> user: what are you?
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>>> assistant: I am a Chatbot intended to give a python program
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>>> user: hmm, can you write a python program to print Hii Heloo
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>>> assistant: Sure Here is a python code.\n print("Hii Heloo")
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>>> user: Can you write a Linear search program in python
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>>> """
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>>> res = generate_text(prompt)
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>>> res
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```
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