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Model: ujwal00/qwen3-1.7b-json
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- json
+- json-schema
+- structured-output
+- qlora
+- function-calling
+datasets:
+- ujwal00/jsonschema-sft
+language:
+- en
+---
+
+# Qwen3-1.7B-JSON — native JSON-Schema compliance fine-tune
+
+Fine-tune of **Qwen/Qwen3-1.7B** that emits JSON conforming to a requested
+**JSON Schema** *natively* — no grammar-constrained decoding — with a large
+gain on the hard case: **nested / `$ref` / `$defs` schemas**.
+
+- **Base model:** Qwen/Qwen3-1.7B (Apache-2.0)
+- **Method:** QLoRA SFT (Unsloth), r=16, α=32, 2 epochs, NEFTune α=5
+- **Data:** ~4k schema→valid-instance pairs generated from JSONSchemaBench
+  *train-split* schemas (72% nested), every target validated with a
+  Draft-2020-12 validator; val/test schemas hash-excluded (decontaminated)
+- **Eval:** native (unconstrained, greedy) schema-compliance, scored with the
+  Draft-2020-12 validator + full format checker
+
+## Results (held-out, measured identically base vs fine-tuned)
+
+Measured on 150–200 held-out schemas the model never trained on. Fine-tuned
+result independently cross-checked via the merged fp16 model (74% nested).
+
+| Metric | Base Qwen3-1.7B | **Fine-tuned** | Δ |
+|---|---|---|---|
+| Nested / `$ref` schemas | 58% | **72–74%** | **+14–16** |
+| Flat schemas | 87% | **92%** | +5 |
+| Overall | 72.5% | **82–83%** | +10 |
+| Parse-fail rate | 4% | **2.5–4%** | ~flat |
+
+## Official results — JSONSchemaBench test split (n=360, native/unconstrained)
+
+Base Qwen3-1.7B vs fine-tuned, identical prompt + Draft-2020-12 validator, greedy.
+
+| Dataset | Base | Fine-tuned | Gain |
+|---|---|---|---|
+| Github_trivial | 49% | **82%** | +33% |
+| Github_easy | 78% | **93%** | +16% |
+| Github_medium | 42% | **56%** | +13% |
+| Github_hard | 11% | **24%** | +13% |
+| Glaiveai2K | 98% | **96%** | -2% |
+| Kubernetes | 53% | **67%** | +13% |
+| Snowplow | 33% | **73%** | +40% |
+| JsonSchemaStore | 31% | **42%** | +11% |
+| **Overall** | 49% | **67%** | +17% |
+
+**McNemar paired test:** 76 schemas fixed, 14 broke, p=1.78e-11 (highly significant).
+
+Our base measurement aligns with the published Llama-3.2-1B native numbers
+(confirming comparability); the fine-tune beats published small-model native
+compliance on nearly every dataset.
+
+## Why this matters
+
+Small models are widely deployed for narrow, high-volume, structured tasks —
+but their published *native* schema-compliance collapses on complex schemas
+(JSONSchemaBench reports ~13–38% for small models on the hardest datasets).
+This fine-tune lifts native compliance on exactly that hard regime, so the
+model is more usable **without** attaching a constrained-decoding engine
+(and better as a base *with* one).
+
+## How to use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json, torch
+
+m = AutoModelForCausalLM.from_pretrained("ujwal00/qwen3-1.7b-json", dtype=torch.float16, device_map="auto")
+tok = AutoTokenizer.from_pretrained("ujwal00/qwen3-1.7b-json")
+
+SYSTEM = ("You are a precise JSON generator. Given a JSON Schema, output ONE JSON value "
+          "that strictly validates against it. Output ONLY the JSON value.")
+schema = {"type":"object","properties":{"name":{"type":"string"},"age":{"type":"integer"}},"required":["name","age"]}
+msgs = [{"role":"system","content":SYSTEM},
+        {"role":"user","content":f"Generate a JSON value that validates against this JSON Schema:\n\n{json.dumps(schema)}\n\nReturn only the JSON value."}]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, enable_thinking=False, return_tensors="pt").to(m.device)
+out = m.generate(ids, max_new_tokens=512, do_sample=False)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+
+**Low-VRAM machines** (e.g. a 6 GB laptop GPU): add `low_cpu_mem_usage=True` to
+`from_pretrained` (and optionally `max_memory={0: "5GiB", "cpu": "6GiB"}`) so
+loading doesn't exhaust memory. On a normal 8 GB+ GPU the snippet above works as-is.
+
+### Verified live output
+
+| Schema | Model output | Valid? |
+|---|---|---|
+| `{name:str, age:int}` (required both) | `{"name": "sample_value", "age": 50}` | ✅ |
+| nested: user{id,email:email-format, roles:enum[]} | `{"user": {"id": 50, "email": "user@example.com", "roles": ["user"]}}` | ✅ |
+
+## Limitations (stated honestly)
+
+- Numbers are **native / unconstrained**; constrained decoding scores differently and is out of scope.
+- Trained on JSONSchemaBench-style schemas; may generalize less to very different schema dialects.
+- Ultra-large schemas (>4000 chars) were excluded from training data.
+- Reported gains are on held-out gold; the official test-split table is the definitive comparison.
+
+## Reproducibility
+
+Data recipe, generator, validator, training + eval code, and manifest are in the
+project repo (`jsonc/`, `kaggle/`). Seed=42; prompt hashed; validator format
+self-tested. Base and fine-tuned measured with identical prompt/validator/greedy.

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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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 tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = message.content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (message.content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = message.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 content = message.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 message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.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 reasoning_content) %}
+                {{- '<|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 %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "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"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

model.safetensors

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+oid sha256:338eb3bdc9360cd56ba56410623b14d69e8971e55c670a8c4c42fb604582caf2
+size 3440091664

special_tokens_map.json

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+{
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+oid sha256:d6a49b89b342f6170968c7f040872d3ab70ad29a224ee9d03f937be6f15c7271
+size 11423191

tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
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+  "errors": "replace",
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+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}