Instructions to use Fralet/DDeduPModelv7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fralet/DDeduPModelv7 with Transformers:

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

pipe = pipeline("text-generation", model="Fralet/DDeduPModelv7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fralet/DDeduPModelv7")
model = AutoModelForCausalLM.from_pretrained("Fralet/DDeduPModelv7")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Fralet/DDeduPModelv7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fralet/DDeduPModelv7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fralet/DDeduPModelv7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Fralet/DDeduPModelv7

SGLang

How to use Fralet/DDeduPModelv7 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 "Fralet/DDeduPModelv7" \
    --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": "Fralet/DDeduPModelv7",
		"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 "Fralet/DDeduPModelv7" \
        --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": "Fralet/DDeduPModelv7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use Fralet/DDeduPModelv7 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 Fralet/DDeduPModelv7 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 Fralet/DDeduPModelv7 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fralet/DDeduPModelv7 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Fralet/DDeduPModelv7",
    max_seq_length=2048,
)

Docker Model Runner
How to use Fralet/DDeduPModelv7 with Docker Model Runner:
```
docker model run hf.co/Fralet/DDeduPModelv7
```

Fralet commited on Feb 10

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4935470

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@@ -16,11 +16,6 @@ datasets:
 - Fralet/DDeduPDatasetv2
 ---
-# Uploaded finetuned  model
-- **Developed by:** **Fralet**
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
 ## Overview
 **DDeduPModelv7** is a fine-tuned version of the Meta-Llama-3-8B model, specifically optimized for educational tasks for Kazakhstan and the world. This model excels in mapping courses to relevant learning outcome codes across various academic disciplines, such as environmental science, pedagogy, pharmacology, ecology, IT, psychology, geodesy, art, linguistics, agriculture, geology, land management, and mathematics.

 - Fralet/DDeduPDatasetv2
 ---
 ## Overview
 **DDeduPModelv7** is a fine-tuned version of the Meta-Llama-3-8B model, specifically optimized for educational tasks for Kazakhstan and the world. This model excels in mapping courses to relevant learning outcome codes across various academic disciplines, such as environmental science, pedagogy, pharmacology, ecology, IT, psychology, geodesy, art, linguistics, agriculture, geology, land management, and mathematics.