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

Libraries

How to use Mic-Fundraiser/fundraising-coach-en with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mic-Fundraiser/fundraising-coach-en")
model = AutoModelForCausalLM.from_pretrained("Mic-Fundraiser/fundraising-coach-en", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mic-Fundraiser/fundraising-coach-en with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mic-Fundraiser/fundraising-coach-en"
# 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-coach-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mic-Fundraiser/fundraising-coach-en

SGLang

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

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

Fundraising Coach — English

A conversational language model fine-tuned exclusively on nonprofit fundraising coaching in English. Produces structured answers: diagnosis → options → recommendation. Trained to avoid fabricating statistics or specific legal/tax guidance.

Base: Qwen/Qwen2.5-7B-Instruct. Dataset: Mic-Fundraiser/fundraising-english-sft (10,085 train + 530 val, distilled from 14 reference texts).

Try it

Live demo on Hugging Face Spaces

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

SYSTEM = (
    "You are an experienced nonprofit fundraising coach. For any substantive question, "
    "structure your answer as: (1) a brief diagnosis of what the core issue really is, "
    "(2) the main options with their tradeoffs, (3) a concrete recommendation with the "
    "reasoning behind it. Be direct, grounded, and practical."
)
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Our annual fund is flat. What should I look at first?"},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
                                          return_tensors="pt", return_dict=False).to(model.device)
out = model.generate(input_ids, max_new_tokens=500, do_sample=True,
                     temperature=0.6, top_p=0.9, repetition_penalty=1.05)
print(tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))

Training


Method	SFT + QLoRA (4-bit)
LoRA	r=32, α=64, dropout=0.05
Modules	q/k/v/o/gate/up/down
Epochs	2
LR	1.5e-4 · cosine
Eff. batch	16 · grad accum 8
Seq length	2048 · packing enabled
Hardware	NVIDIA L40S (HF Jobs)
Framework	TRL + PEFT + transformers

Limitations

Knowledge is shaped by the 14 source texts; regulation, platforms, and trends post-2023 are underrepresented.
Deliberately refuses to invent precise statistics. May feel evasive on "what percentage of donors..." questions.
English-only. For Italian see fundraising-assistant.
2048-token context during training: long multi-turn sessions may lose earlier details.

Downloads last month: 30

Safetensors

Model size

8B params

Tensor type

F16

Model tree for Mic-Fundraiser/fundraising-coach-en

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2821)

this model

Mic-Fundraiser
/

fundraising-coach-en