Instructions to use ao9000/phi2-cda-religion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ao9000/phi2-cda-religion with Transformers:

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

pipe = pipeline("text-generation", model="ao9000/phi2-cda-religion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ao9000/phi2-cda-religion")
model = AutoModelForCausalLM.from_pretrained("ao9000/phi2-cda-religion", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ao9000/phi2-cda-religion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ao9000/phi2-cda-religion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ao9000/phi2-cda-religion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ao9000/phi2-cda-religion

SGLang

How to use ao9000/phi2-cda-religion 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 "ao9000/phi2-cda-religion" \
    --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": "ao9000/phi2-cda-religion",
		"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 "ao9000/phi2-cda-religion" \
        --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": "ao9000/phi2-cda-religion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ao9000/phi2-cda-religion with Docker Model Runner:
```
docker model run hf.co/ao9000/phi2-cda-religion
```

ao9000 commited on Jul 22, 2024

Commit

dab4aa6

verified ·

1 Parent(s): b651d16

Update README.md

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README.md +22 -3

README.md CHANGED Viewed

@@ -1,3 +1,22 @@
----
-license: mit
----

+---
+license: mit
+---
+# Lora finetuning on Wikipedia-10, applying counter factual data augmentation (CDA)
+- Dataset: Wikipedia-10
+- Target modules = ["q_proj", "k_proj", "v_proj", "dense", "fc1", "fc2"]
+```
+{
+    "epoch": 2.8503986104306773,
+    "total_flos": 1.0451807295707516e+18,
+    "train_loss": 0.7933661967515946,
+    "train_runtime": 65423.383,
+    "train_samples": 22453,
+    "train_samples_per_second": 0.978,
+    "train_steps_per_second": 0.031
+}
+```
+# Training script: https://github.com/ao9000/bias-bench/blob/main/experiments/run_clm.py