Instructions to use joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full", trust_remote_code=True)
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

vLLM

How to use joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full

SGLang

How to use joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full 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 "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full" \
    --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": "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full",
		"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 "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full" \
        --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": "joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full with Docker Model Runner:
```
docker model run hf.co/joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full
```

This is a merged version of joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc

Model Card for Model ID

Base Model: https://huggingface.co/edumunozsala/phi3-mini-4k-qlora-python-code-20k
Preference Dataset: https://huggingface.co/datasets/joshuasundance/mypo-4k-rfc
Training Code: https://gist.github.com/joshuasundance-swca/a94672960733782865932a645587ccdc
Training Metrics: trainer_state.json

This is an experimental model made by using joshuasundance/mypo-4k-rfc for DPO training of edumunozsala/phi3-mini-4k-qlora-python-code-20k.

The goal is to learn about model training and potentially get the base model to reliably produce Python with type hints. I chose edumunozsala/phi3-mini-4k-qlora-python-code-20k because I was able to train this model in one hour on my laptop.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Joshua Sundance Bailey
Model type: phi 3 qlora DPO
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: edumunozsala/phi3-mini-4k-qlora-python-code-20k

Model Sources [optional]

Training Code: https://gist.github.com/joshuasundance-swca/a94672960733782865932a645587ccdc

Uses

For evaluation and testing only. Do not expect great results, and do not use this model for anything important. It has not been evaluated in any way after training.

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

Original qlora: iamtarun/python_code_instructions_18k_alpaca
DPO: joshuasundance/mypo-4k-rfc

Training Procedure

See training code using peft, transformers, and trl

Preprocessing [optional]

See training code using peft, transformers, and trl

Training Hyperparameters

See training code using peft, transformers, and trl

Speeds, Sizes, Times [optional]

See trainer_state.json in this repo

[More Information Needed]

Evaluation

See trainer_state.json in this repo

Testing Data, Factors & Metrics

Testing Data

20% of DPO dataset (see training code)

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

Joshua Sundance Bailey

Model Card Contact

Joshua Sundance Bailey

Downloads last month: 3

Safetensors

Model size

4B params

Tensor type

F32

F16

Dataset used to train joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full

Collection including joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full

MyPO Project

Collection

MyPO models, datasets, training assets, and live dashboard for typed Python preference tuning. • 10 items • Updated Apr 23

Paper for joshuasundance/phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc-full

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 48