Instructions to use d4rkninja/tanpo-fundraising with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4rkninja/tanpo-fundraising with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="d4rkninja/tanpo-fundraising") 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("d4rkninja/tanpo-fundraising") model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-fundraising", 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 d4rkninja/tanpo-fundraising with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "d4rkninja/tanpo-fundraising" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "d4rkninja/tanpo-fundraising", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/d4rkninja/tanpo-fundraising
- SGLang
How to use d4rkninja/tanpo-fundraising 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 "d4rkninja/tanpo-fundraising" \ --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": "d4rkninja/tanpo-fundraising", "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 "d4rkninja/tanpo-fundraising" \ --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": "d4rkninja/tanpo-fundraising", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use d4rkninja/tanpo-fundraising with Docker Model Runner:
docker model run hf.co/d4rkninja/tanpo-fundraising
Commit History
27 days ago
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Maintainability cleanup: short Tanpo Family, LFM 1.0 (lfm-1.0) license, no BEATS_BASE / trust_remote_code; GGUF quant table (audit t222u)
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docs: tighten model-card prose (facts unchanged)
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docs: include Fundraising in Tanpo model family table
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Upload evaluation/base_compare/base_fundraising/scores.json with huggingface_hub
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Upload evaluation/ft/fundraising/scores.json with huggingface_hub
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Upload evaluation/ft/SCORECARD.md with huggingface_hub
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Upload evaluation/COMPARE.json with huggingface_hub
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Upload evaluation/COMPARE_FUNDRAISING.md with huggingface_hub
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docs: add evaluation artifacts (tasks, rubric, COMPARE headline)
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docs: polish model card to Tanpo family standard
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Replace in place: tanpo-fundraising merged (PEFT, no Unsloth)
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initial commit
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