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

Libraries

How to use davzoku/trained_expert with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davzoku/trained_expert")
model = AutoModelForCausalLM.from_pretrained("davzoku/trained_expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use davzoku/trained_expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davzoku/trained_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": "davzoku/trained_expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davzoku/trained_expert

SGLang

How to use davzoku/trained_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 "davzoku/trained_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": "davzoku/trained_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 "davzoku/trained_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": "davzoku/trained_expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use davzoku/trained_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 davzoku/trained_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 davzoku/trained_expert to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

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

davzoku commited on Mar 19, 2025

Commit

e2c0447

verified ·

1 Parent(s): f4658f8

Update README.md

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README.md +32 -6

README.md CHANGED Viewed

@@ -9,14 +9,40 @@ tags:
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** davzoku
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-Instruct
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 license: apache-2.0
 language:
 - en
+datasets:
+- davzoku/moecule-finqa
+- davzoku/moecule-kyc
+- davzoku/moecule-stock-market-outlook
 ---
+# 🫐🥫 trained_expert
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/63c51d0e72db0f638ff1eb82/yLsrUZma5WnLzJrhI6ldZ.png" width="150" height="150" alt="logo">
+</p>
+## Model Details
+This model is a domain-specific expert model for [Moecule](https://huggingface.co/collections/davzoku/moecule-67dabc6bb469dcd00ad2a7c5) family of MoE models.
+It is part of [Moecule Ingredients](https://huggingface.co/collections/davzoku/moecule-ingredients-67dac0e6210eb1d95abc6411) and all relevant expert models, LoRA adapters, and datasets can be found there.
+### Additional Information
+- QLoRA 4-bit fine-tuning with Unsloth
+- Base Model: `unsloth/llama-3-8b-Instruct`
+## The Team
+- CHOCK Wan Kee
+- Farlin Deva Binusha DEVASUGIN MERLISUGITHA
+- GOH Bao Sheng
+- Jessica LEK Si Jia
+- Sinha KHUSHI
+- TENG Kok Wai (Walter)
+## References
+- [Unsloth Tutorial](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama)
+- [Unsloth Finetuning Colab Notebook](<https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb#scrollTo=uMuVrWbjAzhc>)