Instructions to use Mic-Fundraiser/fundraising-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mic-Fundraiser/fundraising-assistant with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mic-Fundraiser/fundraising-assistant")
model = AutoModelForCausalLM.from_pretrained("Mic-Fundraiser/fundraising-assistant")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mic-Fundraiser/fundraising-assistant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mic-Fundraiser/fundraising-assistant

SGLang

How to use Mic-Fundraiser/fundraising-assistant 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 "Mic-Fundraiser/fundraising-assistant" \
    --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": "Mic-Fundraiser/fundraising-assistant",
		"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 "Mic-Fundraiser/fundraising-assistant" \
        --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": "Mic-Fundraiser/fundraising-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mic-Fundraiser/fundraising-assistant with Docker Model Runner:
```
docker model run hf.co/Mic-Fundraiser/fundraising-assistant
```

Fundraising Assistant

Modello conversazionale in italiano specializzato in fundraising e raccolta fondi per il non profit.

Fine-tuning (SFT + LoRA, poi merge) di Qwen/Qwen2.5-3B-Instruct sul dataset Mic-Fundraiser/fundraising-italian-sft.

Caratteristiche

Stile: competente, diretto, tono da "giovane professore"
Nessuna premessa inutile, nessuna antitesi, niente elenchi infiniti
Ottimizzato per scrivere DEM, lettere, proposte per il non profit

Uso

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Mic-Fundraiser/fundraising-assistant"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "Sei un esperto di fundraising per il non profit."},
    {"role": "user", "content": "Come convinco un donatore occasionale a passare a ricorrente?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=400, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Training

Base: Qwen2.5-3B-Instruct (QLoRA 4-bit)
LoRA: r=16, alpha=32, dropout=0.05, target=attn+MLP
Epoche: 3, LR: 2e-4, cosine schedule
Hardware: NVIDIA L4 via HF Jobs
Dataset: 1273 esempi in formato OpenAI messages

Limitazioni

Modello da 3B parametri: rapido e adatto a inferenza CPU/GPU leggera, ma su ragionamenti complessi o contesti molto lunghi resta inferiore a modelli più grandi. Il dataset è piccolo (1273 esempi): il fine-tuning impone uno stile, non nuova conoscenza fattuale.

Downloads last month: 140

Safetensors

Model size

3B params

Tensor type

F16

Model tree for Mic-Fundraiser/fundraising-assistant

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Adapter

(1270)

this model

Mic-Fundraiser
/

fundraising-assistant

Fundraising Assistant

Caratteristiche

Uso

Training

Limitazioni

Model tree for Mic-Fundraiser/fundraising-assistant

Dataset used to train Mic-Fundraiser/fundraising-assistant

Space using Mic-Fundraiser/fundraising-assistant 1