Instructions to use fedora-copr/phi-2-snippets-logdetective with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fedora-copr/phi-2-snippets-logdetective with Transformers:

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

pipe = pipeline("text-generation", model="fedora-copr/phi-2-snippets-logdetective")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fedora-copr/phi-2-snippets-logdetective")
model = AutoModelForCausalLM.from_pretrained("fedora-copr/phi-2-snippets-logdetective")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fedora-copr/phi-2-snippets-logdetective with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fedora-copr/phi-2-snippets-logdetective"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fedora-copr/phi-2-snippets-logdetective",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fedora-copr/phi-2-snippets-logdetective

SGLang

How to use fedora-copr/phi-2-snippets-logdetective 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 "fedora-copr/phi-2-snippets-logdetective" \
    --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": "fedora-copr/phi-2-snippets-logdetective",
		"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 "fedora-copr/phi-2-snippets-logdetective" \
        --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": "fedora-copr/phi-2-snippets-logdetective",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fedora-copr/phi-2-snippets-logdetective with Docker Model Runner:
```
docker model run hf.co/fedora-copr/phi-2-snippets-logdetective
```

Model Card for Model ID

Model Details

Model Description

Developed by: Jiri Podivin jpodivin@redhat.com
Model type: phi-2
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: microsoft/phi-2

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

fedora-copr/autoannotated_snippets_mistral

Factors

[More Information Needed]

Metrics

Rouge metric was used to compare model outputs with expected annotations from test subset.

Results

[More Information Needed]

Summary

Technical Specifications

Compute Infrastructure

Single node

Hardware

1 * GeForce RTX 4090

Software

transformers
peft

Model Card Authors [optional]

Jiri Podivin jpodivin@redhat.com

Downloads last month: 6

Safetensors

Model size

3B params

Tensor type

BF16

fedora-copr
/

phi-2-snippets-logdetective

Model Card for Model ID

Model Details

Model Description

Model Sources [optional]

Uses

Direct Use

Downstream Use [optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary

Technical Specifications

Compute Infrastructure

Hardware

Software

Model Card Authors [optional]

Dataset used to train fedora-copr/phi-2-snippets-logdetective