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Model: Turhan123/astra-meal-parser
Source: Original Platform

.gitattributes

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README.md

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+---
+license: other
+license_name: qwen-research
+license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
+base_model: Qwen/Qwen2.5-3B-Instruct
+language:
+- tr
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- unsloth
+- qwen2.5
+- lora
+- sft
+- meal-parsing
+- nutrition
+- calorie-estimation
+- turkish
+- structured-output
+- json
+---
+
+# 🥗 Astra Meal Parser (v1)
+
+A fine-tuned **Qwen2.5-3B-Instruct** model that reads a free-text meal description in
+**Turkish or English** and turns it into a clean, structured list of food items and their
+amounts — ready to feed into a deterministic nutrition calculator.
+
+The model **does not** estimate calories or macros itself. It only parses. This is a
+deliberate design choice (see *Why parsing only?* below) that keeps nutrition accuracy
+high and easy to maintain.
+
+```
+"2 yumurta, 100g tavuk göğsü ve 1 muz"
+        │
+        ▼  (this model — parsing)
+{"items": [
+  {"name": "Yumurta",     "amount": "2 adet"},
+  {"name": "Tavuk Göğsü", "amount": "100g"},
+  {"name": "Muz",         "amount": "1 adet"}
+]}
+        │
+        ▼  (nutrition table + calculator — not part of this model)
+{ totalCalories, totalProtein, totalCarbs, totalFat, items[...] }
+```
+
+- **Developed by:** Turhan Göksu
+- **Model type:** Causal LM adapter merged into base weights (Qwen2.5-3B)
+- **Languages:** Turkish, English, and mixed/code-switched input
+- **Finetuned from:** [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
+
+## Why parsing only?
+
+An earlier version asked the model to output calories and macros directly. It plateaued at
+~25% calorie error with a systematic overestimation bias: a language model cannot reliably
+memorize accurate per-food nutrition values, especially for foods with high natural variance.
+
+Splitting the problem fixed this. The model now does the one thing language models are good
+at — understanding messy natural language — and a static nutrition table + a small
+calculator handle the arithmetic deterministically. Result: calorie error dropped from ~25%
+to ~3%, and any remaining error is fixable by editing the table, **without retraining**.
+
+## Output format
+
+The model is trained to return **only** a strict JSON object:
+
+```json
+{"items": [{"name": "string", "amount": "string"}]}
+```
+
+No prose, no markdown, no macros — just the JSON.
+
+## System prompt
+
+Use this exact system prompt for best results:
+
+```
+You are a meal parser. Extract every food item and its amount from the user's meal
+description (Turkish or English). Return ONLY a strict JSON object of the form
+{"items": [{"name": string, "amount": string}]}. No macros, no calories, no
+conversational text, no markdown, only valid JSON.
+```
+
+## Uses
+
+### Direct use
+- Meal logging / calorie tracking apps where users type meals in natural language.
+- Bilingual and code-switched input such as `"200g grilled chicken ve 1 kase pirinç"`.
+- A drop-in front end for a deterministic nutrition pipeline.
+
+### Out-of-scope use
+- **Standalone nutrition estimation.** This model only extracts items and amounts; it does
+  not produce calories or macros on its own.
+- **Medical or dietary prescriptions.** Output is informational, not medical advice.
+- **Open-ended conversation.** The model is specialized for structured parsing and is not
+  intended as a general assistant.
+
+## How to get started
+
+> **Note on inference.** This is a custom merged model and is **not served by the free
+> Hugging Face Serverless Inference API**. Run it locally with `transformers`, convert it to
+> GGUF for on-device / `llama.cpp` use, or deploy a dedicated Inference Endpoint.
+
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Turhan123/astra-meal-parser"  # pin a version with revision="v1"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision="v1")
+model = AutoModelForCausalLM.from_pretrained(model_id, revision="v1", device_map="auto")
+
+SYSTEM = (
+    "You are a meal parser. Extract every food item and its amount from the user's "
+    "meal description (Turkish or English). Return ONLY a strict JSON object of the form "
+    '{"items": [{"name": string, "amount": string}]}. '
+    "No macros, no calories, no conversational text, no markdown, only valid JSON."
+)
+
+def parse(meal: str):
+    messages = [{"role": "system", "content": SYSTEM},
+                {"role": "user", "content": meal}]
+    ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
+                                        return_tensors="pt").to(model.device)
+    out = model.generate(ids, max_new_tokens=256, do_sample=False)
+    text = tokenizer.decode(out[0][ids.shape[-1]:], skip_special_tokens=True)
+    return json.loads(text)
+
+print(parse("2 yumurta, 100g tavuk göğsü ve 1 muz"))
+# {'items': [{'name': 'Yumurta', 'amount': '2 adet'}, ...]}
+```
+
+## Training
+
+| | |
+|---|---|
+| Base model | `Qwen/Qwen2.5-3B-Instruct` (4-bit QLoRA via Unsloth) |
+| Method | Supervised fine-tuning, LoRA (r=16, α=32) on q/k/v/o/gate/up/down |
+| Data | 778 meal→items examples (Turkish / English / mixed); 739 train / 39 eval |
+| Schedule | 3 epochs, 279 steps, lr 2e-4, batch 4 × grad-accum 2, linear decay |
+| Optimizer | AdamW 8-bit, weight decay 0.01 |
+| Hardware | Single NVIDIA T4 (~20 min) |
+| Export | LoRA merged into 16-bit weights |
+
+## Evaluation
+
+Held-out set of **94 meal descriptions** (53 Turkish, 34 English, 7 mixed), with zero
+overlap with the training data. Parsing metrics score the model output directly; nutrition
+metrics reflect the **full pipeline** (this parser + nutrition table + calculator).
+
+**Parsing**
+
+| Metric | Value |
+|---|---|
+| Item Precision / Recall / F1 | 100% / 100% / 100% |
+| Parse failures | 0 / 94 |
+| Unresolved foods (table gaps) | 0 |
+
+**Nutrition (full pipeline)**
+
+| Metric | Value |
+|---|---|
+| Calorie MAPE | 3.1% |
+| Within ±15% | 85 / 91 |
+| Protein / Carbs / Fat MAE | 0.5 g / 1.5 g / 0.4 g |
+
+**Calorie MAPE by language**
+
+| Language | MAPE | n |
+|---|---|---|
+| Turkish | 3.3% | 51 |
+| English | 2.7% | 33 |
+| Mixed (TR/EN) | 3.4% | 7 |
+
+## Bias, risks, and limitations
+
+- **Parsing only.** Calorie/macro accuracy depends on the accompanying nutrition table and
+  calculator, which are not part of this repository.
+- **Portion ambiguity.** Vague amounts (e.g. "1 bowl of rice") are resolved with default
+  serving sizes; the true amount may differ. This is the dominant source of residual error.
+- **Table coverage.** Foods outside the nutrition table cannot be scored downstream;
+  long-tail coverage is the main lever for production accuracy and is addressed by expanding
+  the table, not by retraining.
+- **JSON robustness.** Output is valid JSON in the large majority of cases, but consuming
+  applications should still guard against an occasional malformed response (e.g. retry once).
+
+## Versioning
+
+Versions are published as git tags on this repository. Pin a specific version in production
+with `revision="v1"`. Future improvements are added as new tags (`v2`, `v3`, …) without
+breaking pinned consumers.
+
+## Technical references
+
+- Qwen2.5 Technical Report — [arXiv:2412.15115](https://arxiv.org/abs/2412.15115)
+- LoRA: Low-Rank Adaptation of Large Language Models — [arXiv:2106.09685](https://arxiv.org/abs/2106.09685)
+- Unsloth — [github.com/unslothai/unsloth](https://github.com/unslothai/unsloth)
+
+## Acknowledgements
+
+Built on [Qwen2.5](https://huggingface.co/Qwen) by the Qwen team, and trained efficiently
+with [Unsloth](https://github.com/unslothai/unsloth).
+
+## License
+
+Fine-tuned from `Qwen/Qwen2.5-3B-Instruct`; use is subject to the
+[Qwen Research License](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE)
+of the base model.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) 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' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "model_type": "qwen2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.6.1",
+    "use_cache": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

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+    }
+  },
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}