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Model: kartikey31/txn-parser
Source: Original Platform

gemma-3-270m/README.md

@@ -0,0 +1,152 @@
+---
+license: apache-2.0
+base_model: unsloth/gemma-3-270m-it
+tags:
+  - text-generation
+  - lora
+  - qlora
+  - gguf
+  - transaction-parser
+language:
+  - en
+  - hi
+library_name: peft
+---
+
+# txn-parser / gemma-3-270m
+
+QLoRA fine-tune of [`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it) 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 **`gemma-3-270m/`** 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
+
+- `gemma-3-270m/adapters/` — PEFT LoRA adapter (rank 32). Load on top of the base model
+  with `peft.PeftModel.from_pretrained(base, "kartikey31/txn-parser", subfolder="gemma-3-270m/adapters")`.
+- `gemma-3-270m/gguf/` — merged GGUF builds at multiple quantization levels
+  (file names follow `txn-parser-gemma-3-270m-<QUANT>.gguf`):
+  - [`gemma-3-270m/gguf/txn-parser-gemma-3-270m-F16.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/gemma-3-270m/gguf/txn-parser-gemma-3-270m-F16.gguf)  (542.8 MB)
+  - [`gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q4_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q4_K_M.gguf)  (253.1 MB)
+  - [`gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q5_K_M.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q5_K_M.gguf)  (260.0 MB)
+  - [`gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q6_K.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q6_K.gguf)  (283.0 MB)
+  - [`gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q8_0.gguf`](https://huggingface.co/kartikey31/txn-parser/resolve/main/gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q8_0.gguf)  (291.5 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 | `unsloth/gemma-3-270m-it` |
+| 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 | 4 |
+| Max seq length | 1024 |
+| Learning rate | 2e-4 (warmup 3%) |
+| Started |  |
+| Finished |  |
+
+## 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 "$".
+- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
+- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
+- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
+- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
+- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
+- Multi-transaction inputs become multiple array entries in spoken order.
+- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.
+```
+
+Source of truth: [`scripts/_lib.py`](https://github.com/kartikeychoudhary/txn-parser/blob/main/scripts/_lib.py)
+constant `SYSTEM_PROMPT`. Don't retype it — pull from `_lib.py` or this README.
+
+## Download a single GGUF
+
+```bash
+huggingface-cli download kartikey31/txn-parser \
+    gemma-3-270m/gguf/txn-parser-gemma-3-270m-Q4_K_M.gguf \
+    --local-dir .
+```
+
+## Inference (Python, llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+SYSTEM_PROMPT = '''You convert voice-transcribed transaction descriptions into structured JSON.
+
+Output ONLY a JSON object with this schema, no other text:
+{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}
+
+Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.
+
+Rules:
+- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
+- 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-gemma-3-270m-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-gemma-3-270m-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 gemma-3-270m
+```
+
+---
+*Auto-published by `scripts/train_and_publish.py` on 2026-05-22T21:33:31.274220+00:00.*

gemma-3-270m/adapters/README.md

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+---
+base_model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:unsloth/gemma-3-270m-it-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

gemma-3-270m/adapters/adapter_config.json

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+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Gemma3ForCausalLM",
+    "parent_library": "transformers.models.gemma3.modeling_gemma3",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "down_proj",
+    "gate_proj",
+    "k_proj",
+    "o_proj",
+    "q_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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gemma-3-270m/adapters/chat_template.jinja

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+{# Unsloth Chat template fixes #}
+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- elif message['content'] is defined -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}
+
+{# Copyright 2025-present Unsloth. Apache 2.0 License. #}

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