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---
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
- text-generation
- lora
- qlora
- gguf
- transaction-parser
language:
- en
- hi
library_name: peft
---
# txn-parser / qwen3-0.6b
QLoRA fine-tune of [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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 **`qwen3-0.6b/`** 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
- `qwen3-0.6b/adapters/` — PEFT LoRA adapter (rank 32). Load on top of the base model
with `peft.PeftModel.from_pretrained(base, "kartikey31/txn-parser", subfolder="qwen3-0.6b/adapters")`.
- `qwen3-0.6b/gguf/` — merged GGUF builds at multiple quantization levels
(file names follow `txn-parser-qwen3-0.6b-<QUANT>.gguf`):
- [`qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-F16.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-F16.gguf) (1198.2 MB)
- [`qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q4_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q4_K_M.gguf) (396.7 MB)
- [`qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q5_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q5_K_M.gguf) (444.4 MB)
- [`qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q6_K.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q6_K.gguf) (495.1 MB)
- [`qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q8_0.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q8_0.gguf) (639.4 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 | `Qwen/Qwen3-0.6B` |
| Method | QLoRA (4-bit) via Unsloth |
| LoRA rank | 32 (alpha 64, dropout 0.0) |
| Epochs | 2 |
| Batch size (train) | 64 |
| Grad accumulation | 2 |
| Eval batch size | 16 |
| Max seq length | 1024 |
| Learning rate | 2e-4 (warmup 3%) |
| Started | 2026-05-22T21:54:03.967900+00:00 |
| Finished | 2026-05-22T22:42:56.089528+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 "$".
- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
- Multi-transaction inputs become multiple array entries in spoken order.
- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.
```
Source of truth: [`scripts/_lib.py`](https://github.com/kartikeychoudhary/txn-parser/blob/main/scripts/_lib.py)
constant `SYSTEM_PROMPT`. Don't retype it — pull from `_lib.py` or this README.
## Download a single GGUF
```bash
huggingface-cli download kartikey31/txn-parser \
qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q4_K_M.gguf \
--local-dir .
```
## Inference (Python, llama-cpp-python)
```python
from llama_cpp import Llama
SYSTEM_PROMPT = '''You convert voice-transcribed transaction descriptions into structured JSON.
Output ONLY a JSON object with this schema, no other text:
{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}
Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.
Rules:
- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
- 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-qwen3-0.6b-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-qwen3-0.6b-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 qwen3-0.6b
```
---
*Auto-published by `scripts/train_and_publish.py` on 2026-05-22T22:42:56.387243+00:00.*

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---
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen3-0.6b-unsloth-bnb-4bit
- 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[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
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## 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. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.19.1

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{
"alora_invocation_tokens": null,
"alpha_pattern": {},
"arrow_config": null,
"auto_mapping": {
"base_model_class": "Qwen3ForCausalLM",
"parent_library": "transformers.models.qwen3.modeling_qwen3",
"unsloth_fixed": true
},
"base_model_name_or_path": "unsloth/qwen3-0.6b-unsloth-bnb-4bit",
"bias": "none",
"corda_config": null,
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"megatron_config": null,
"megatron_core": "megatron.core",
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
{%- set current_content = message.content if message.content is defined and message.content is not none else '' %}
{%- set tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = current_content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (current_content|length) - tool_end_length %}
{%- if start_pos < 0 %}
{%- set start_pos = 0 %}
{%- endif %}
{%- set end_of_message = current_content[start_pos:] %}
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set m_content = message.content if message.content is defined and message.content is not none else '' %}
{%- set content = m_content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in m_content %}
{%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
{%- set reasoning_content = (m_content.split('</think>')|first).rstrip('\n') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and (not reasoning_content.strip() == '')) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"model_max_length": 40960,
"pad_token": "<|PAD_TOKEN|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"single_word": false,
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"special": true
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"content": "<|object_ref_start|>",
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"151647": {
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"151648": {
"content": "<|box_start|>",
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"151651": {
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"special": true
},
"151654": {
"content": "<|vision_pad|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
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