Instructions to use RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf with Ollama:
ollama run hf.co/RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/Sharathhebbar24_-_code_gpt2_mini_model-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Sharathhebbar24_-_code_gpt2_mini_model-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
code_gpt2_mini_model - GGUF
- Model creator: https://huggingface.co/Sharathhebbar24/
- Original model: https://huggingface.co/Sharathhebbar24/code_gpt2_mini_model/
Original model description:
license: apache-2.0 datasets: - HuggingFaceH4/ultrachat_200k - mlabonne/CodeLlama-2-20k - Intel/orca_dpo_pairs language: - en pipeline_tag: text-generation tags: - gpt2 - dpo
This model is a finetuned version of Sharathhebbar24/chat_gpt2_dpo using mlabonne/CodeLlama-2-20k
Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This 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 of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token i only use the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a prompt.
To use this model
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/chat_gpt2"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> return generated[:generated.rfind(".")+1]
>>> prompt = """
>>> user: what are you?
>>> assistant: I am a Chatbot intended to give a python program
>>> user: hmm, can you write a python program to print Hii Heloo
>>> assistant: Sure Here is a python code.\n print("Hii Heloo")
>>> user: Can you write a Linear search program in python
>>> """
>>> res = generate_text(prompt)
>>> res
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