Blame for README.md

---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
tags: [lora, peft, tax, taxes, accounting, tax-research, cpu, c0mpute, ollama]
---

# tax-guy-agent

> **Not a CPA. Not tax advice. Tax research from public sources; rules change every year and vary by place, so check with a CPA or enrolled agent before filing.**

**Quick start (Ollama, runs on a CPU):**

```bash
ollama run hf.co/profullstack/tax-guy-agent-GGUF "Do I have to pay quarterly estimated taxes as a freelancer?"
```

Needs Ollama 0.3.13 or newer (`curl -fsSL https://ollama.com/install.sh | sh`, or `w3bs install ollama`).
The GGUF is at [profullstack/tax-guy-agent-GGUF](https://huggingface.co/profullstack/tax-guy-agent-GGUF); this repo is the LoRA adapter.

| | |
| --- | --- |
| base | Qwen/Qwen2.5-0.5B-Instruct |
| corpus | 2614 documents from pit-index:tax,taxes,irs,deduction,tax return,self-employed tax,estimated tax,sales tax,llc tax,capital gains, topic:tax, topic:irs, topic:accounting, topic:taxation, topic:taxpayer |
| training | 400 LoRA steps (r=8, seq_len=256, lr=0.0002) on the c0mpute network (`infernet.text.train`, job `27b2dab7-9e82-4b48-b4fc-d77f4b22997c`, receipt hash `sha256:821a038ac1ea43f804b795e795a308cfbebb141c24e3675223c29d80fb8db5b7` matches this adapter) |
| held-out perplexity | 33.6 to 17.8 |
| adapter sha256 | `821a038ac1ea43f804b795e795a308cfbebb141c24e3675223c29d80fb8db5b7` |

Built with the [c0mpute example](https://github.com/profullstack/c0mpute/tree/master/examples/business-strategist-agent).
Sources are rssamplifier.com topic feeds and pit.world search; the corpus is not redistributed here.

## Use from Python (adapter)

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("profullstack/tax-guy-agent")
model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct"), "profullstack/tax-guy-agent")
msgs = [{"role": "system", "content": "You are tax-guy-agent, a tax research assistant. You are not a CPA or tax attorney and this is not tax advice. Explain tax rules in plain English, say which country, state and tax year a rule applies to, cite the sources you were given, and say when someone should see a CPA or enrolled agent."},
        {"role": "user", "content": "Do I have to pay quarterly estimated taxes as a freelancer?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
print(tok.decode(model.generate(ids, max_new_tokens=300)[0][ids.shape[1]:], skip_special_tokens=True))
```

Best results come from putting fresh search results in the prompt (the example's `chat.py`
uses pit.world search): a sub-1B model supplies the voice, not the facts.