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Model: gauthierpiarrette/nl2jq-qwen3-0.6b
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-0.6B
+tags:
+- jq
+- code-generation
+- json
+- lora
+datasets:
+- gauthierpiarrette/nl2jq
+metrics:
+- execution-accuracy
+---
+
+# nl2jq-qwen3-0.6b
+
+*Part of the **nl2jq** project — [all artifacts](https://huggingface.co/collections/gauthierpiarrette/nl2jq-natural-language-to-jq-locally-6a5299203df3832415275223) · [code + CLI](https://github.com/gauthierpiarrette/nl2jq) · [live demo](https://huggingface.co/spaces/gauthierpiarrette/nl2jq) · [benchmark](https://huggingface.co/datasets/gauthierpiarrette/nl2jq-bench)*
+
+A LoRA fine-tune of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) that translates a
+natural-language request plus a JSON sample into a [jq](https://jqlang.github.io/jq/)
+program. It is the **practical baseline** companion to the from-scratch
+[`nl2jq-40m`](https://huggingface.co/gauthierpiarrette/nl2jq-40m): larger, but its
+pretrained language priors make it markedly more reliable at **copying novel field names**
+from your JSON — which is what most real CLI use needs.
+
+The LoRA adapter has been merged into the base weights, so it loads as an ordinary model.
+
+**This is the recommended default backend of the `jqgen` CLI** — the best
+latency/accuracy balance for local use (~1–3s per query on laptop CPU).
+
+## Usage
+
+```bash
+cat data.json | jqgen --backend qwen "total spend per customer, paid orders only"
+```
+
+Or directly:
+```python
+import re
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("gauthierpiarrette/nl2jq-qwen3-0.6b")
+model = AutoModelForCausalLM.from_pretrained("gauthierpiarrette/nl2jq-qwen3-0.6b")
+msgs = [{"role": "system", "content": "You translate a natural-language request plus a "
+         "sample of JSON into a single jq program. Output only the jq program, nothing else."},
+        {"role": "user", "content": "Request: highest paid person\nJSON sample: [...]"}]
+enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
+ids = enc["input_ids"]  # newer transformers return a BatchEncoding here, not a tensor
+out = tok.decode(model.generate(ids, max_new_tokens=128)[0][ids.shape[1]:],
+                 skip_special_tokens=True)
+# Qwen3 emits an (empty) reasoning block before the program — strip it:
+print(re.sub(r"(?s)^\s*<think>.*?</think>\s*", "", out).strip())
+```
+
+## Results
+
+Execution accuracy on the frozen 400-item
+[`nl2jq-bench v1.0.0`](https://huggingface.co/datasets/gauthierpiarrette/nl2jq-bench)
+(0% field overlap with training, novel domains, one-shot evaluation). **These weights are
+the v6 fine-tune** — trained on data with per-example-unique field names and coverage of
+`reduce`/`foreach`/`walk`/`paths`/`if` constructs; the superseded v5 row is kept for the
+record:
+
+| Model | frozen pass@1 | valid | T1 | T2 | T3 | T4 | T5-gen |
+|---|---|---|---|---|---|---|---|
+| **this model (v6)** | **0.40** | 0.73 | 0.65 | 0.52 | 0.36 | 0.28 | **0.20** |
+| v5 fine-tune (superseded) | 0.24 | 0.67 | 0.45 | 0.38 | 0.21 | 0.12 | 0.02 |
+| Qwen3-0.6B base (zero-shot) | 0.01 | 0.06 | 0.02 | 0.00 | 0.00 | 0.00 | 0.02 |
+| [nl2jq-qwen3.5-2b](https://huggingface.co/gauthierpiarrette/nl2jq-qwen3.5-2b) (bigger sibling, CLI config) | 0.48 | 0.82 | 0.75 | 0.61 | 0.47 | 0.35 | 0.18 |
+| Claude Opus 4.8 (zero-shot, context row) | 0.96 | 0.98 | 1.00 | 0.96 | 0.96 | 0.96 | 0.90 |
+
+Three honest readings:
+
+- **Fine-tuning does all the work at this scale**: the base model zero-shot scores 0.01
+  (it emits pseudo-code, not jq); this LoRA takes it to 0.40 on held-out fields.
+- **The v6 data fix mattered more than anything else**: same base, same LoRA recipe,
+  +67% pass@1 and 10× T5 purely from making field-copying the only winning strategy in
+  training.
+- **This is the fast backend, not the most accurate one**: it answers in ~1–3s on laptop
+  CPU; the 2B sibling is +8 points at ~3× the latency. Both remain far from the frontier's
+  0.96 — that gap is disclosed, not hidden.
+
+## Training
+
+- **Base**: Qwen3-0.6B. **Method**: LoRA (r=32, α=64, all-linear targets), 1 epoch over
+  150k execution-verified examples, cosine schedule, bf16, gradient checkpointing.
+- **Data**: the v6 generation of the synthetic pipeline — per-example-unique field names
+  (so copying from the input, not vocabulary recall, is the only winning strategy) plus
+  generators for `reduce`/`foreach`/`walk`/`paths`/`if`-banding constructs. 100% synthetic
+  and execution-verified — see the
+  [nl2jq dataset](https://huggingface.co/datasets/gauthierpiarrette/nl2jq). No web text, no
+  scraped code. Every program was run under jq 1.7.1 and kept only if it produced
+  non-degenerate output.
+- Frontier-model outputs are **never** used as training targets; all natural-language
+  phrasings are template/grammar-generated by the author.
+
+## Limitations
+
+- jq 1.7.1 only; no streaming, modules, or user-defined functions.
+- Ambiguous requests may yield a valid program that answers a different reading.
+- **Inspect before trusting the output** — the `jqgen` CLI prints the program to stderr.
+  jq runs locally with no network access, but an incorrect program can still mislead.
+- Not a general assistant — it emits jq and nothing else.
+
+## License
+
+Apache-2.0 (inherits the base model's license). Fine-tuned on CC BY 4.0 synthetic data.
+
+Code, benchmark tooling, and training pipeline: [github.com/gauthierpiarrette/nl2jq](https://github.com/gauthierpiarrette/nl2jq)

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 message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].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' }}
+        {{- 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": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

eval_results.json

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+{
+  "benchmark": "gauthierpiarrette/nl2jq-bench",
+  "version": "1.0.0",
+  "freeze_sha256": "25e7c01360707b30df93e7c1ed26997b3bb3ee7840a093d024bb4d16f489c5de",
+  "jq_version": "1.7.1",
+  "n_items": 400,
+  "protocol": "one-shot; no selection or iteration against the frozen set",
+  "model": "gauthierpiarrette/nl2jq-qwen3-0.6b",
+  "results": [
+    {
+      "system": "nl2jq-qwen3-0.6b-v6",
+      "decoding": "greedy",
+      "pass_at_1": 0.4,
+      "valid_at_1": 0.73,
+      "tiers": {
+        "T1": 0.65,
+        "T2": 0.52,
+        "T3": 0.36,
+        "T4": 0.28,
+        "T5": 0.2
+      },
+      "t5_generalization": 0.2
+    }
+  ]
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.13.0"
+}

model.safetensors

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+oid sha256:1b9f227b582c165fd64dd7c090646c9617c814e5268d84226576701ee28eba41
+size 1192135096

tokenizer.json

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+oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
+size 11422650

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}