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Model: joshelu/qwen3-4b-eventspec-martech-merged
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+- martech
+- analytics
+- event-taxonomy
+- json
+- structured-output
+- sft
+- lora
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Qwen3-4B EventSpec — MarTech Event Taxonomy Generator (merged)
+
+Fine-tuned **Qwen/Qwen3-4B-Instruct-2507** that converts free-form marketing tracking
+requests into clean, implementation-ready **analytics event specifications** as strict JSON.
+
+This repository contains the **fully merged weights** (LoRA adapter merged into the base
+model), so it loads like any standard model — no PEFT/adapter step required.
+
+- **Base model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+- **LoRA adapter (pre-merge):** [joshelu/qwen3-4b-Instruct-eventspec-martech-sft](https://huggingface.co/joshelu/qwen3-4b-Instruct-eventspec-martech-sft)
+- **Method:** Supervised Fine-Tuning (SFT) + LoRA, then merged
+- **Task:** marketing tracking request → strict-JSON EventSpec
+
+## Intended use
+
+Give the model a plain-English marketing/analytics tracking request; it returns a single
+JSON object specifying the events to implement (event names, parameters, triggers, consent
+and deduplication requirements, QA criteria, risk flags, etc.). Useful for MarTech / analytics
+engineering, GA4 / GTM instrumentation planning, and taxonomy standardization.
+
+## ⚠️ Prompt format (required for correct output)
+
+The model was trained with a specific chat format. **You must reproduce it** or output
+quality degrades sharply.
+
+**System prompt (use verbatim):**
+
+```
+You are EventSpec, an expert MarTech analytics engineer. You convert free-form marketing tracking requests into clean, implementation-ready analytics event specifications.
+
+Given a marketing tracking request, respond with a SINGLE valid JSON object and nothing else: no prose, no markdown, no code fences. The JSON must be strictly parseable.
+
+The specification captures: a concise `request_summary`; the `business_goal`; the `tracking_scope` (platforms, page_or_screen, user_action, conversion_type); and a list of `recommended_events`. Each recommended event defines `event_name` (snake_case), `event_description`, `platform`, `event_type`, `required_parameters`, `optional_parameters`, `trigger_condition`, `consent_requirements`, and `deduplication_requirements`.
+
+Use consistent snake_case event and parameter names, follow analytics best practices (GA4/GTM conventions where relevant), and respect privacy/consent requirements. Output only the JSON object.
+```
+
+**User message format:**
+
+```
+Convert this marketing tracking request into a clean analytics event specification.
+
+Request:
+<your tracking request here>
+```
+
+## Usage (transformers)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "joshelu/qwen3-4b-eventspec-martech-merged"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
+
+SYSTEM_PROMPT = (
+    "You are EventSpec, an expert MarTech analytics engineer. You convert free-form "
+    "marketing tracking requests into clean, implementation-ready analytics event "
+    "specifications.\n\n"
+    "Given a marketing tracking request, respond with a SINGLE valid JSON object and "
+    "nothing else: no prose, no markdown, no code fences. The JSON must be strictly "
+    "parseable.\n\n"
+    "The specification captures: a concise `request_summary`; the `business_goal`; the "
+    "`tracking_scope` (platforms, page_or_screen, user_action, conversion_type); and a "
+    "list of `recommended_events`. Each recommended event defines `event_name` "
+    "(snake_case), `event_description`, `platform`, `event_type`, `required_parameters`, "
+    "`optional_parameters`, `trigger_condition`, `consent_requirements`, and "
+    "`deduplication_requirements`.\n\n"
+    "Use consistent snake_case event and parameter names, follow analytics best "
+    "practices (GA4/GTM conventions where relevant), and respect privacy/consent "
+    "requirements. Output only the JSON object."
+)
+USER_PREFIX = "Convert this marketing tracking request into a clean analytics event specification."
+
+request = ("A pharmacy app wants to track refill reminders, refill started, refill submitted, "
+           "refill ready, and pickup completed, but no medication names or prescription numbers "
+           "should be sent.")
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": f"{USER_PREFIX}\n\nRequest:\n{request}"},
+]
+prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+
+out = model.generate(**inputs, max_new_tokens=2048, do_sample=False,
+                     pad_token_id=tok.eos_token_id)
+print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Usage (Inference Endpoints / chat_completion)
+
+```python
+from huggingface_hub import InferenceClient
+client = InferenceClient("https://YOUR-ENDPOINT.endpoints.huggingface.cloud", token="hf_...")
+resp = client.chat_completion(
+    messages=[
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": f"{USER_PREFIX}\n\nRequest:\n{request}"},
+    ],
+    max_tokens=2048,
+    temperature=0,
+)
+print(resp.choices[0].message.content)
+```
+
+## Recommended generation settings
+
+- `temperature = 0` (greedy) — best for stable, strictly parseable JSON.
+- `max_new_tokens >= 2048` — specs are long; a lower limit will truncate the JSON mid-string.
+- Parse the output with `json.loads`; retry with a higher token limit if parsing fails.
+
+## Output schema
+
+The model produces a single JSON object. Core keys:
+
+- `request_summary` — one-line summary of the request.
+- `business_goal` — the measurement objective.
+- `tracking_scope` — `{ platforms, page_or_screen, user_action, conversion_type }`.
+- `recommended_events` — list of events, each with `event_name` (snake_case),
+  `event_description`, `platform`, `event_type`, `required_parameters`,
+  `optional_parameters`, `trigger_condition`, `consent_requirements`,
+  `deduplication_requirements`.
+
+Richer examples in the training data also include `implementation_notes`, `qa_criteria`,
+`open_questions`, and `risk_flags`, which the model produces as appropriate.
+
+## Training
+
+- **Dataset:** `joshelu/martech-event-taxonomy-mapper-training-data` (private).
+  - The 100-row `train` split, **minus** every `id` appearing in the `validation` or `test`
+    split → **80 training rows** (held-out eval stays honest).
+  - Eval during training used the `validation` split (10 rows).
+  - Assistant targets are compact, strict JSON (`json.dumps(output, separators=(",", ":"))`),
+    validated as parseable before training.
+- **Method:** SFT + LoRA, then merged.
+  - LoRA: `r=16`, `alpha=32`, `dropout=0.05`,
+    target modules `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`.
+  - 3 epochs, effective batch size 4 (per-device 1 × grad-accum 4), lr `2e-4`,
+    warmup ratio `0.05`, cosine schedule, max length 2048, bf16, gradient checkpointing.
+  - Hardware: a10g-small (HF Jobs).
+- **Metrics (final):** eval loss ≈ `0.320`, eval mean token accuracy ≈ `0.924`.
+
+## Limitations
+
+- Trained on a small (80-row) dataset — coverage is limited to the taxonomy and styles seen
+  in training; unusual domains may produce weaker specs.
+- Output is a strong **draft**, not a substitute for review by an analytics engineer,
+  especially for consent/privacy and PII handling.
+- With very low `max_new_tokens`, long specs will be truncated and fail JSON parsing — keep
+  the limit high and validate the parse.

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 %}
+{%- 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" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- 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' }}
+{%- 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": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "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": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "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
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.13.0"
+}

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+size 8044982080

tokenizer.json

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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": false,
+  "local_files_only": false,
+  "model_max_length": 1010000,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}