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Model: joshelu/qwen3-4b-eventspec-martech-merged Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags:
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- martech
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- analytics
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- event-taxonomy
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- json
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- structured-output
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- sft
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- lora
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Qwen3-4B EventSpec — MarTech Event Taxonomy Generator (merged)
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Fine-tuned **Qwen/Qwen3-4B-Instruct-2507** that converts free-form marketing tracking
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requests into clean, implementation-ready **analytics event specifications** as strict JSON.
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This repository contains the **fully merged weights** (LoRA adapter merged into the base
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model), so it loads like any standard model — no PEFT/adapter step required.
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- **Base model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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- **LoRA adapter (pre-merge):** [joshelu/qwen3-4b-Instruct-eventspec-martech-sft](https://huggingface.co/joshelu/qwen3-4b-Instruct-eventspec-martech-sft)
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- **Method:** Supervised Fine-Tuning (SFT) + LoRA, then merged
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- **Task:** marketing tracking request → strict-JSON EventSpec
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## Intended use
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Give the model a plain-English marketing/analytics tracking request; it returns a single
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JSON object specifying the events to implement (event names, parameters, triggers, consent
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and deduplication requirements, QA criteria, risk flags, etc.). Useful for MarTech / analytics
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engineering, GA4 / GTM instrumentation planning, and taxonomy standardization.
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## ⚠️ Prompt format (required for correct output)
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The model was trained with a specific chat format. **You must reproduce it** or output
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quality degrades sharply.
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**System prompt (use verbatim):**
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```
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You are EventSpec, an expert MarTech analytics engineer. You convert free-form marketing tracking requests into clean, implementation-ready analytics event specifications.
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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.
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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`.
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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.
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```
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**User message format:**
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```
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Convert this marketing tracking request into a clean analytics event specification.
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Request:
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<your tracking request here>
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```
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## Usage (transformers)
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "joshelu/qwen3-4b-eventspec-martech-merged"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
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SYSTEM_PROMPT = (
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"You are EventSpec, an expert MarTech analytics engineer. You convert free-form "
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"marketing tracking requests into clean, implementation-ready analytics event "
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"specifications.\n\n"
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"Given a marketing tracking request, respond with a SINGLE valid JSON object and "
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"nothing else: no prose, no markdown, no code fences. The JSON must be strictly "
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"parseable.\n\n"
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"The specification captures: a concise `request_summary`; the `business_goal`; the "
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"`tracking_scope` (platforms, page_or_screen, user_action, conversion_type); and a "
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"list of `recommended_events`. Each recommended event defines `event_name` "
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"(snake_case), `event_description`, `platform`, `event_type`, `required_parameters`, "
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"`optional_parameters`, `trigger_condition`, `consent_requirements`, and "
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"`deduplication_requirements`.\n\n"
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"Use consistent snake_case event and parameter names, follow analytics best "
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"practices (GA4/GTM conventions where relevant), and respect privacy/consent "
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"requirements. Output only the JSON object."
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)
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USER_PREFIX = "Convert this marketing tracking request into a clean analytics event specification."
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request = ("A pharmacy app wants to track refill reminders, refill started, refill submitted, "
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"refill ready, and pickup completed, but no medication names or prescription numbers "
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"should be sent.")
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"{USER_PREFIX}\n\nRequest:\n{request}"},
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]
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=2048, do_sample=False,
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pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## Usage (Inference Endpoints / chat_completion)
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient("https://YOUR-ENDPOINT.endpoints.huggingface.cloud", token="hf_...")
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resp = client.chat_completion(
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"{USER_PREFIX}\n\nRequest:\n{request}"},
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],
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max_tokens=2048,
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temperature=0,
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)
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print(resp.choices[0].message.content)
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```
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## Recommended generation settings
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- `temperature = 0` (greedy) — best for stable, strictly parseable JSON.
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- `max_new_tokens >= 2048` — specs are long; a lower limit will truncate the JSON mid-string.
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- Parse the output with `json.loads`; retry with a higher token limit if parsing fails.
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## Output schema
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The model produces a single JSON object. Core keys:
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- `request_summary` — one-line summary of the request.
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- `business_goal` — the measurement objective.
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- `tracking_scope` — `{ platforms, page_or_screen, user_action, conversion_type }`.
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- `recommended_events` — list of events, each with `event_name` (snake_case),
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`event_description`, `platform`, `event_type`, `required_parameters`,
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`optional_parameters`, `trigger_condition`, `consent_requirements`,
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`deduplication_requirements`.
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Richer examples in the training data also include `implementation_notes`, `qa_criteria`,
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`open_questions`, and `risk_flags`, which the model produces as appropriate.
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## Training
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- **Dataset:** `joshelu/martech-event-taxonomy-mapper-training-data` (private).
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- The 100-row `train` split, **minus** every `id` appearing in the `validation` or `test`
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split → **80 training rows** (held-out eval stays honest).
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- Eval during training used the `validation` split (10 rows).
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- Assistant targets are compact, strict JSON (`json.dumps(output, separators=(",", ":"))`),
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validated as parseable before training.
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- **Method:** SFT + LoRA, then merged.
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- LoRA: `r=16`, `alpha=32`, `dropout=0.05`,
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target modules `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`.
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- 3 epochs, effective batch size 4 (per-device 1 × grad-accum 4), lr `2e-4`,
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warmup ratio `0.05`, cosine schedule, max length 2048, bf16, gradient checkpointing.
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- Hardware: a10g-small (HF Jobs).
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- **Metrics (final):** eval loss ≈ `0.320`, eval mean token accuracy ≈ `0.924`.
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## Limitations
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- Trained on a small (80-row) dataset — coverage is limited to the taxonomy and styles seen
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in training; unusual domains may produce weaker specs.
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- Output is a strong **draft**, not a substitute for review by an analytics engineer,
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especially for consent/privacy and PII handling.
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- With very low `max_new_tokens`, long specs will be truncated and fail JSON parsing — keep
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the limit high and validate the parse.
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61
chat_template.jinja
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61
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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71
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 5000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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13
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.13.0"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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||||
oid sha256:910066f6343ad97dd3b84352ad1fa1e902aa2a6aaa257e272ac651d166426c80
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size 8044982080
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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30
tokenizer_config.json
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30
tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
|
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"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
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"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
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"<|box_end|>",
|
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"<|quad_start|>",
|
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"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
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"is_local": false,
|
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"local_files_only": false,
|
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"model_max_length": 1010000,
|
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"pad_token": "<|endoftext|>",
|
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"split_special_tokens": false,
|
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"tokenizer_class": "Qwen2Tokenizer",
|
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"unk_token": null
|
||||
}
|
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