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Model: naazimsnh02/TriageIQ-Qwen3-4B Source: Original Platform
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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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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- itsm
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- service-desk
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- ticket-triage
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- structured-output
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- json
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- lora
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- qwen3
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- rocm
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- amd-mi300x
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---
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# TriageIQ — Qwen3-4B Incident Triage
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**TriageIQ** turns a free-text IT support complaint into a single structured,
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schema-valid JSON incident record. It is the fast-path model behind the
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TriageIQ predictive complaint-triage & SLA-aware routing engine, fine-tuned on
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the **AMD Instinct MI300X**.
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This is `Qwen/Qwen3-4B-Instruct-2507` with a bf16 LoRA fine-tune **merged into
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the base weights** — load it like any standard causal LM; no PEFT required at
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inference time.
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## What it does
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Given a complaint, the model emits **exactly** this JSON object and nothing else:
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```json
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{
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"summary": "One-line normalized incident title",
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"category": "Software | Hardware | Network | Access",
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"urgency": 1,
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"impact": 1,
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"assignment_group": "IT Support | Network Ops",
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"suggested_first_action": "Concrete first remediation step",
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"confidence": 0.0
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}
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```
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- `urgency` / `impact`: integers `1 = High, 2 = Medium, 3 = Low`.
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- `confidence`: float in `[0.0, 1.0]`.
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> **Design note — priority and SLA are *not* model outputs.** Priority (P1–P5)
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> and the SLA target are computed deterministically downstream from
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> `(impact, urgency)` via the ITSM matrix, and the dynamic SLA-breach-risk is a
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> separate explainable engine. The model is deliberately kept to the perception
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> step (text → structured record) so the business logic stays auditable.
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## Intended use
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- Automated first-pass triage of IT service-desk tickets into structured records.
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- Feeding a deterministic routing / SLA engine that consumes the JSON contract.
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- Demo / research within the TriageIQ project (AMD Hackathon — Customer Complaint
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Classification & Routing Engine track).
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**Not intended for** medical, legal, or safety-critical decisions, or as the sole
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authority for ticket prioritization without the downstream deterministic engine.
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## Usage
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The model was trained with a fixed system prompt; use it verbatim for best results.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "naazimsnh02/TriageIQ-Qwen3-4B"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
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SYSTEM_PROMPT = (
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"You are TriageIQ, an IT service-desk triage assistant. Convert the user's "
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"complaint into a single JSON object with EXACTLY these keys: summary, "
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"category, urgency, impact, assignment_group, suggested_first_action, "
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"confidence. "
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"category is one of [Software, Hardware, Network, Access]. "
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"assignment_group is one of [IT Support, Network Ops]. "
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"urgency and impact are integers 1=High, 2=Medium, 3=Low. "
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"confidence is a float 0.0-1.0. "
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"Do NOT include priority or SLA — those are computed elsewhere. "
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"Output ONLY the JSON object, no prose, no code fences."
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)
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complaint = "My laptop won't connect to the office Wi-Fi since this morning and I have a client call in 20 minutes."
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": complaint},
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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=256, do_sample=False)
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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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Serving with vLLM (ROCm or CUDA):
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```bash
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vllm serve naazimsnh02/TriageIQ-Qwen3-4B
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```
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## Evaluation
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Held-out validation set of **75** complaints, greedy decoding, scored with the
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project's `TriageRecord` schema validator (the same contract enforced in
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production). Field metrics are computed over schema-valid outputs.
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| Metric | Base (Qwen3-4B-Instruct) | **TriageIQ (fine-tuned)** |
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|---|---|---|
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| Schema-valid rate | 100.0% | **100.0%** |
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| Category accuracy | 86.7% | **97.3%** (+10.7) |
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| Assignment-group agreement | 90.7% | **96.0%** (+5.3) |
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| Impact agreement | 100.0% | **100.0%** |
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| Urgency agreement | 65.3% | 61.3% |
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The fine-tune's headline gain is **category accuracy (+10.7 pts)** and routing
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accuracy (**assignment-group +5.3 pts**) while preserving a 100% schema-valid
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rate. Urgency — the most subjective field — moves within label noise (a few
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cases on 75); it is also the field whose downstream effect is bounded by the
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deterministic priority matrix rather than the model alone.
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## Training
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| | |
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|---|---|
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| Base model | `Qwen/Qwen3-4B-Instruct-2507` |
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| Method | LoRA (merged), **bf16** — no 4-bit / QLoRA |
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| LoRA config | `r=32, alpha=64, dropout=0.05`, targets: `q,k,v,o,gate,up,down` proj |
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| Objective | Causal LM, **completion-only loss** (prompt masked) |
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| Epochs / seq len | 3 / 1024 |
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| Optimizer / LR | AdamW, `2e-4`, cosine schedule, 3% warmup, effective batch 16 |
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| Framework | TRL `SFTTrainer` + PEFT + Transformers |
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| Hardware | **AMD Instinct MI300X** (gfx942, 192 GB), ROCm, PyTorch 2.8 |
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bf16 LoRA was chosen deliberately: on ROCm, bitsandbytes ≤ 0.49.2 has a 4-bit
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decode NaN bug, and the MI300X's 192 GB removes any need for quantization on a
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4B model — one card co-hosts the fine-tuned model and the live streaming
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pipeline without quantization juggling.
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## Training data
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~432 synthetic English IT complaints (357 train / 75 validation). Built from the
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~100 unique realistic descriptions in
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[`6StringNinja/synthetic-servicenow-incidents`](https://huggingface.co/datasets/6StringNinja/synthetic-servicenow-incidents)
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used **as seeds only** (the raw set has independently shuffled fields and is not
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trained on directly). Seeds were relabeled coherently against a fixed ITSM rubric
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by a `Qwen2.5-32B-Instruct` teacher and paraphrase-augmented for phrasing/persona
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diversity, with an 85/15 split stratified by category and de-leaked at the seed
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level. Labels never include priority or SLA.
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## Limitations & biases
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- Trained on synthetic, English-only, single-paragraph complaints; behavior on
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long threads, multi-language, or out-of-distribution domains is unverified.
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- Two assignment groups (`IT Support`, `Network Ops`) and four categories only —
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it will coerce inputs into this taxonomy.
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- `urgency` is subjective and the weakest field; downstream the deterministic
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priority matrix bounds its impact.
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- Confidence is a learned self-report, not a calibrated probability.
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- For robustness the production pipeline validates every output against the
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schema and falls back to a deterministic rule tagger on invalid JSON — do the
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same when integrating.
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## License
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Apache-2.0, inherited from the `Qwen/Qwen3-4B-Instruct-2507` base model.
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added_tokens.json
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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 }}
|
||||
{{- '", "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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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,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 9728,
|
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"layer_types": [
|
||||
"full_attention",
|
||||
"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",
|
||||
"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,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 5000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.57.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
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"vocab_size": 151936
|
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}
|
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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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"temperature": 0.7,
|
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"top_k": 20,
|
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"top_p": 0.8,
|
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"transformers_version": "4.57.0"
|
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}
|
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merges.txt
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merges.txt
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Load Diff
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model-00001-of-00002.safetensors
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model-00001-of-00002.safetensors
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406
model.safetensors.index.json
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406
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{
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"metadata": {
|
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"total_parameters": 4022468096,
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"total_size": 8044936192
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"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_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|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1010000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user