Instructions to use arshirazi/tiny-log-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arshirazi/tiny-log-parser with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "arshirazi/tiny-log-parser")

Transformers

How to use arshirazi/tiny-log-parser with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arshirazi/tiny-log-parser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arshirazi/tiny-log-parser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/arshirazi/tiny-log-parser

SGLang

How to use arshirazi/tiny-log-parser 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 "arshirazi/tiny-log-parser" \
    --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": "arshirazi/tiny-log-parser",
		"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 "arshirazi/tiny-log-parser" \
        --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": "arshirazi/tiny-log-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use arshirazi/tiny-log-parser 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 arshirazi/tiny-log-parser 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 arshirazi/tiny-log-parser to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arshirazi/tiny-log-parser to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="arshirazi/tiny-log-parser",
    max_seq_length=2048,
)

Docker Model Runner
How to use arshirazi/tiny-log-parser with Docker Model Runner:
```
docker model run hf.co/arshirazi/tiny-log-parser
```

tiny-log-parser / README.md

arshirazi

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	---
	base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
	library_name: peft
	pipeline_tag: text-generation
	license: apache-2.0
	tags:
	- base_model:adapter:unsloth/qwen3-4b-unsloth-bnb-4bit
	- lora
	- transformers
	- unsloth
	- log-parsing
	- structured-output
	---

	# tiny-log-parser

	LoRA adapter for Qwen3-4B that normalizes log lines from six wire formats into a
	canonical 7-field JSON record. Paired with a deterministic epoch pre-pass it
	reaches 100% exact match vs 83.5% for `gemini-3.1-pro-preview` on a
	200-example held-out test set.

	- Code, eval harness, writeup: https://github.com/arshirazi97/tiny-log-parser
	- Runnable demo: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arshirazi97/tiny-log-parser/blob/main/demo.ipynb)
	- Base model: `unsloth/qwen3-4b-unsloth-bnb-4bit`

	## What it does

	Takes a log line in syslog RFC3164, nginx combined, logfmt, Java/log4j,
	container JSON, or a bracketed application format, and emits:

	`timestamp` (ISO8601 UTC, second precision) · `level` (one of CRITICAL, ERROR,
	WARNING, INFO, DEBUG) · `service` · `trace_id` · `status_code` · `latency_ms`
	(integer) · `message`

	## Results

	200-example held-out test set, same spec given to both systems, same exact-match
	verifier, all seven fields must match.

	\| \| Exact match \| 95% CI \| Latency p50 \|
	\|---\|---\|---\|---\|
	\| gemini-3.1-pro-preview (3-shot) \| 83.5% \| 78.5 – 88.5% \| 11,713 ms \|
	\| this adapter alone (zero-shot) \| 73.0% \| 66.5 – 79.0% \| 4,197 ms \|
	\| this adapter + epoch pre-pass \| 100% \| 100 – 100% \| 4,197 ms \|

	The adapter alone loses. Every one of its 54 misses is a bare-epoch timestamp —
	integer division into calendar arithmetic the model cannot do reliably. Scaling
	training data 5k → 20k moved that 0.5 points, so the conversion is routed to
	`datetime.fromtimestamp()` instead of learned. It fires on 41 of 200 inputs.

	## Usage

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

	BASE = "unsloth/qwen3-4b-unsloth-bnb-4bit"
	tok = AutoTokenizer.from_pretrained(BASE)
	model = PeftModel.from_pretrained(
	AutoModelForCausalLM.from_pretrained(BASE, device_map="auto"),
	"arshirazi/tiny-log-parser").eval()
	```

	Requires a CUDA GPU — the base is 4-bit bitsandbytes, which does not run on
	Apple Silicon or CPU. The adapter expects the exact prompt spec in `eval.py`
	(`build_prompt(line, [])`) zero-shot; a different prompt format degrades output.
	The epoch pre-pass lives in `score_hybrid.py`.

	## Training

	4-bit QLoRA, r=16, 2 epochs, response-masked so loss lands on the JSON only.
	20,000 synthetic examples generated canonical-record-first — the label exists
	before the input, so every example is correct by construction. Train and test
	draw from disjoint time windows (Jan–May vs Jun–Jul). Single RTX 2000 Ada
	(16 GB), ~2.5 hours.

	## Limitations

	The test set is synthetic, drawn from the same six renderers as training.
	Disjoint time windows prevent timestamp memorization but not format
	memorization. Read the 100% as "solved within its stated distribution," not as a
	claim about production logs.

	Real logs are harder: multiline stack traces, truncated lines, vendor quirks,
	and formats outside these six are absent. Hand-written lines outside the
	generator's parameter range surfaced two gaps the test set did not catch —
	syslog severity 5/6 mapping, and a placeholder service name invented on a
	truncated line.

	Compared against one baseline, scored once, at temperature 0.

	### Framework versions

	- PEFT 0.20.0