Instructions to use umarfarzan/sideeffect-algorithm-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umarfarzan/sideeffect-algorithm-expert with Transformers:

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

pipe = pipeline("text-generation", model="umarfarzan/sideeffect-algorithm-expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("umarfarzan/sideeffect-algorithm-expert", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use umarfarzan/sideeffect-algorithm-expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umarfarzan/sideeffect-algorithm-expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarfarzan/sideeffect-algorithm-expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umarfarzan/sideeffect-algorithm-expert

SGLang

How to use umarfarzan/sideeffect-algorithm-expert 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 "umarfarzan/sideeffect-algorithm-expert" \
    --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": "umarfarzan/sideeffect-algorithm-expert",
		"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 "umarfarzan/sideeffect-algorithm-expert" \
        --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": "umarfarzan/sideeffect-algorithm-expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use umarfarzan/sideeffect-algorithm-expert with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for umarfarzan/sideeffect-algorithm-expert to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for umarfarzan/sideeffect-algorithm-expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for umarfarzan/sideeffect-algorithm-expert to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="umarfarzan/sideeffect-algorithm-expert",
    max_seq_length=2048,
)

Docker Model Runner
How to use umarfarzan/sideeffect-algorithm-expert with Docker Model Runner:
```
docker model run hf.co/umarfarzan/sideeffect-algorithm-expert
```

umarfarzan commited on Mar 6, 2025

Commit

c9e7566

verified ·

1 Parent(s): 7ba476c

Upload README.md with huggingface_hub

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README.md +20 -2

README.md CHANGED Viewed

@@ -1,3 +1,21 @@
 # Social Media Content Analyzer
@@ -20,7 +38,7 @@ model = AutoModelForCausalLM.from_pretrained(model_id)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 def generate_content_analysis(transcript, confidence_score):
-    prompt = f"""Below is a transcript from a social media video along with its confidence score.
 Your task is to analyze the content and provide a detailed content critique analyzing the hook, reliability factor, relatability, and shareability.
 ### Transcript:
@@ -30,7 +48,7 @@ Your task is to analyze the content and provide a detailed content critique anal
 {confidence_score}
 ### Content Critique:"""
     inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
     outputs = model.generate(
         input_ids=inputs.input_ids,

+---
+language: en
+license: apache-2.0
+tags:
+- social-media
+- content-analysis
+- deepseek
+- llama
+- unsloth
+datasets:
+- custom
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-generation
+widget:
+- text: "Let me show you how to track your expenses with this simple spreadsheet template. First, create columns for date, category, and amount. Then, use the SUM function to automatically calculate your total spending..."
+---
 # Social Media Content Analyzer
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 def generate_content_analysis(transcript, confidence_score):
+    prompt = f"""Below is a transcript from a social media video along with its confidence score.
 Your task is to analyze the content and provide a detailed content critique analyzing the hook, reliability factor, relatability, and shareability.
 ### Transcript:
 {confidence_score}
 ### Content Critique:"""
     inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
     outputs = model.generate(
         input_ids=inputs.input_ids,