Instructions to use LeTG/llama-3p1-8B-psyop-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeTG/llama-3p1-8B-psyop-analysis with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "LeTG/llama-3p1-8B-psyop-analysis")

Transformers

How to use LeTG/llama-3p1-8B-psyop-analysis with Transformers:

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

pipe = pipeline("text-generation", model="LeTG/llama-3p1-8B-psyop-analysis")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LeTG/llama-3p1-8B-psyop-analysis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeTG/llama-3p1-8B-psyop-analysis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeTG/llama-3p1-8B-psyop-analysis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeTG/llama-3p1-8B-psyop-analysis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LeTG/llama-3p1-8B-psyop-analysis

SGLang

How to use LeTG/llama-3p1-8B-psyop-analysis 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 "LeTG/llama-3p1-8B-psyop-analysis" \
    --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": "LeTG/llama-3p1-8B-psyop-analysis",
		"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 "LeTG/llama-3p1-8B-psyop-analysis" \
        --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": "LeTG/llama-3p1-8B-psyop-analysis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LeTG/llama-3p1-8B-psyop-analysis with Docker Model Runner:
```
docker model run hf.co/LeTG/llama-3p1-8B-psyop-analysis
```

Model Card for Model ID

This is a fine-tuned version of Llama-3.1-8B optimized for detecting and analyzing psychological coercion and PSYOP techniques in text. The model was trained on 10,137 annotated examples from YouTube interview transcripts to identify manipulation tactics, target audiences, and sentiment patterns.

Use case: Content moderation, media literacy analysis, interview transcript analysis

Developed by: Thomas Giroux
Model type: Fine-tuned Language Model
Language(s) (NLP): English
Finetuned from model: meta-llama/Llama-3.1-8B

Training Details

Training Data: psychological-coercion-identification dataset (12.7k examples) Training Procedure: LoRA fine-tuning with 2 epochs Hyperparameters:

Learning rate: 2e-4 Batch size: 4 (per device) LoRA rank: 8 Training loss: 1.071 (final) Validation loss: 1.075 (final)

PEFT 0.18.1

Downloads last month: 6

Model tree for LeTG/llama-3p1-8B-psyop-analysis

Base model

meta-llama/Llama-3.1-8B

Adapter

(1085)

this model