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---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M-Instruct
tags:
- text-generation
- lora
- qlora
- gguf
- transaction-parser
language:
- en
- hi
library_name: peft
---
# txn-parser / smollm2-360m
QLoRA fine-tune of [`HuggingFaceTB/SmolLM2-360M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) for
extracting structured transaction data (amount, currency, item, category,
type) from free-form Indian-English / code-switched speech and text.
This model lives in subfolder **`smollm2-360m/`** of the
[`kartikey31/txn-parser`](https://huggingface.co/kartikey31/txn-parser) repo, alongside
sibling fine-tunes of other base models trained on the same data.
## What's in here
- `smollm2-360m/adapters/` — PEFT LoRA adapter (rank 32). Load on top of the base model
with `peft.PeftModel.from_pretrained(base, "kartikey31/txn-parser", subfolder="smollm2-360m/adapters")`.
- `smollm2-360m/gguf/` — merged GGUF builds at multiple quantization levels
(file names follow `txn-parser-smollm2-360m-<QUANT>.gguf`):
- [`smollm2-360m/gguf/txn-parser-smollm2-360m-F16.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/smollm2-360m/gguf/txn-parser-smollm2-360m-F16.gguf) (725.6 MB)
- [`smollm2-360m/gguf/txn-parser-smollm2-360m-Q4_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/smollm2-360m/gguf/txn-parser-smollm2-360m-Q4_K_M.gguf) (270.6 MB)
- [`smollm2-360m/gguf/txn-parser-smollm2-360m-Q5_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/smollm2-360m/gguf/txn-parser-smollm2-360m-Q5_K_M.gguf) (289.9 MB)
- [`smollm2-360m/gguf/txn-parser-smollm2-360m-Q6_K.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/smollm2-360m/gguf/txn-parser-smollm2-360m-Q6_K.gguf) (367.4 MB)
- [`smollm2-360m/gguf/txn-parser-smollm2-360m-Q8_0.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/smollm2-360m/gguf/txn-parser-smollm2-360m-Q8_0.gguf) (386.4 MB)
## Training data
- 93,348 teacher-labeled examples (`data/distill/train.jsonl`)
- 300 held-out eval examples (`data/distill/eval.jsonl`)
- Validator-gated: every row's `output` passes the project's grammar +
amount-parser semantic validator.
## Training config
| Knob | Value |
|---|---|
| Base model | `HuggingFaceTB/SmolLM2-360M-Instruct` |
| Method | QLoRA (4-bit) via Unsloth |
| LoRA rank | 32 (alpha 64, dropout 0.0) |
| Epochs | 2 |
| Batch size (train) | 64 |
| Grad accumulation | 1 |
| Eval batch size | 16 |
| Max seq length | 1024 |
| Learning rate | 2e-4 (warmup 3%) |
| Started | 2026-05-22T21:26:43.776899+00:00 |
| Finished | 2026-05-22T21:30:14.660709+00:00 |
## System prompt (use this EXACTLY)
The model was trained with one specific system prompt and Gemma/Smol/Qwen
chat template. If you paraphrase the prompt or skip the chat template,
quality degrades quickly. Copy-paste this verbatim into your inference
client (no leading/trailing whitespace, no edits):
```text
You convert voice-transcribed transaction descriptions into structured JSON.
Output ONLY a JSON object with this schema, no other text:
{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}
Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.
Rules:
- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
- Multi-transaction inputs become multiple array entries in spoken order.
- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.
```
Source of truth: [`scripts/_lib.py`](https://github.com/kartikeychoudhary/txn-parser/blob/main/scripts/_lib.py)
constant `SYSTEM_PROMPT`. Don't retype it — pull from `_lib.py` or this README.
## Download a single GGUF
```bash
huggingface-cli download kartikey31/txn-parser \
smollm2-360m/gguf/txn-parser-smollm2-360m-Q4_K_M.gguf \
--local-dir .
```
## Inference (Python, llama-cpp-python)
```python
from llama_cpp import Llama
SYSTEM_PROMPT = '''You convert voice-transcribed transaction descriptions into structured JSON.
Output ONLY a JSON object with this schema, no other text:
{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}
Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.
Rules:
- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
- Multi-transaction inputs become multiple array entries in spoken order.
- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.'''
llm = Llama(
model_path="txn-parser-smollm2-360m-Q4_K_M.gguf",
n_gpu_layers=-1, n_ctx=2048,
)
out = llm.create_chat_completion(
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "200 ka samosa"},
],
temperature=0.0,
)
print(out["choices"][0]["message"]["content"])
```
## Inference (CLI, llama.cpp)
```bash
./llama-cli -m txn-parser-smollm2-360m-Q4_K_M.gguf \
--grammar-file scripts/grammar.gbnf \
--system-prompt "$(cat system_prompt.txt)" \
-p "200 ka samosa" -n 256
```
## Reproduce
```bash
git clone https://github.com/kartikeychoudhary/txn-parser.git
cd txn-parser && bash setup.sh
python scripts/train_and_publish.py --only smollm2-360m
```
---
*Auto-published by `scripts/train_and_publish.py` on 2026-05-22T21:30:14.964463+00:00.*

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---
base_model: HuggingFaceTB/SmolLM2-360M-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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### Framework versions
- PEFT 0.19.1

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