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Model: Jalik/qwen3-8b-codev-sva-ol-dfs-think 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/Qwen3-8B
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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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- systemverilog
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- sva
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- assertion-generation
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- formal-verification
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- eda
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- nl2sva
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- reasoning
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---
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# Qwen3-8B — NL2SVA with OL-NL / DFS reasoning
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Qwen3-8B fully fine-tuned to translate a natural-language description of a
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design property into a SystemVerilog Assertion (SVA), emitting an explicit,
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structured derivation inside `<think>...</think>` before the final assertion.
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The reasoning is **not** free-form chain of thought. Each assistant turn walks a
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fixed three-part structure derived mechanically from the golden assertion's
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operator tree:
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1. **OL NL** — an *operator-level* restatement of the requirement, grounded in
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the design's real signal names and aligned to the assertion's top-level
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operator structure.
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2. **nl-decomposition tree** — a top-down tree, one node per operator, each
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carrying its natural-language piece and why that operator expresses it.
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3. **operator-merge-sva tree** — a bottom-up symbolic derivation labelling every
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node `T1, T2, ...`, where each line is written in terms of earlier labels.
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## Prompt format
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Trained with the `qwen3` chat template. The user turn contains the testbench
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module (including a `tb_reset` wire and a `// TODO: ASSERTION` marker) followed
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by `Question:` and the property description.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Jalik/qwen3-8b-codev-sva-ol-dfs-think"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
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messages = [
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{"role": "system", "content":
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"You are an AI assistant tasked with formal verification of register "
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"transfer level (RTL) designs.\nYour job is to translate a description "
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"of an assertion to concrete SystemVerilog Assertion (SVA) implementation."},
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{"role": "user", "content": """Here is the testbench to perform your translation:
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module example(input clk, input rst_n, output valid, output fail);
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// ... design body ...
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wire tb_reset;
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assign tb_reset = (rst_n == 1'b0);
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// TODO: ASSERTION
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endmodule
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Question: Create a SVA assertion that checks: The valid and fail signals must never be active at the same time. Use the signals 'fail' and 'valid'.
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You should use `tb_reset` as the disable condition signal. Do not add code to output an error message string.
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Enclose your SVA code with ```systemverilog and ```. Only output the code snippet and do NOT output anything else.
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Answer:"""},
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]
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text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True,
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enable_thinking=True)
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out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=2048)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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`enable_thinking=True` matters: the model was trained with the reasoning block
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present and loss computed over it. Disabling it diverges from training.
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## Output shape
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```
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<think>
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OL NL: valid and fail must never be true at the same time.
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***nl-decomposition tree***
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operator: !
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natural language piece: valid and fail must never be true at the same time.
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reason: The logical NOT (!) negates the conjunction (valid && fail), ...
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└── operator: &&
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├── operator: null (leaf: valid)
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└── operator: null (leaf: fail)
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***operator-merge-sva tree***
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T1 = fail
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T2 = valid
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T3 = T2 && T1 [ = valid && fail ]
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T4 = !(T3) [ = !(valid && fail) ]
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Final assertion (T4): !(valid && fail)
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</think>
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```systemverilog
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asrt_valid_fail_mutex: assert property (@(posedge clk) disable iff (tb_reset)
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!(valid && fail)
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);
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```
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```
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The answer is always the final fenced `systemverilog` block, so downstream
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extractors that take the *last* such fence work unchanged.
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## Training data
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5,000 records sampled from
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[`wyt2000/CodeV-SVA-datasets`](https://huggingface.co/datasets/wyt2000/CodeV-SVA-datasets)
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(83,195 records), filtered to those whose answer is a single `assert property`
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parseable into an operator/signal tree.
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The reasoning was generated, not hand-written, and — importantly — **formally
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validated**. For each record an OL-NL statement was produced, a *candidate* SVA
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was regenerated from that statement alone, and the candidate was checked against
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the golden assertion with JasperGold. Only a **`Full equivalence`** result was
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accepted; anything weaker (including one-directional implication) was rejected
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and the record retried or replaced. So each retained OL-NL statement is known to
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be a semantically exact restatement of its assertion, not merely a plausible one.
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System and user turns are byte-identical to the source dataset; only the
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assistant turn was replaced.
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## Training
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| | |
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|---|---|
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| Base | `Qwen/Qwen3-8B` (36 layers, hidden 4096, bf16) |
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| Method | Full-parameter SFT (not LoRA) |
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| Framework | LLaMA-Factory 0.9.3.dev0, DeepSpeed ZeRO-3 |
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| Hardware | 4 × NVIDIA H200 |
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| Epochs | 3 (1,875 steps) |
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| LR | 1e-5, cosine, warmup ratio 0.1 |
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| Effective batch | 8 (1 × 2 grad-accum × 4 GPUs) |
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| Max sequence | 16,384 tokens |
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| Precision | bfloat16 |
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| Final train loss | **0.1725** |
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| Runtime | 68 min |
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Loss fell from ~1.76 to ~0.12 with gradient norm settling from ~35 to ~0.5.
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## Limitations
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- **Convention-bound.** Training data consistently uses `tb_reset` as the
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`disable iff` condition and a testbench with a `// TODO: ASSERTION` marker.
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Prompts departing from that shape may degrade.
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- **Single-assertion scope.** Trained only on examples whose answer is exactly
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one `assert property`. Multi-assertion or multi-clock requests are out of
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distribution.
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- **Not verified end-to-end here.** Training loss is not correctness. The
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reasoning traces in the *training data* were JasperGold-verified, but this
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model card reports no benchmark score for the fine-tuned model itself — treat
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generated assertions as proposals to be formally checked, not as correct by
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construction.
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- **Reasoning is generated.** The decomposition trees come from an LLM pipeline;
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the operator structure is mechanical, but the natural-language justifications
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in the `reason:` fields were not individually reviewed.
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## License
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Released under Apache-2.0, matching the `Qwen/Qwen3-8B` base model. Training data
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derives from `wyt2000/CodeV-SVA-datasets`; consult that dataset for its own terms.
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added_tokens.json
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config.json
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|
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
241
tokenizer_config.json
Normal file
241
tokenizer_config.json
Normal file
@@ -0,0 +1,241 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
||||
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||||
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|
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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"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,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- 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>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"epoch": 3.0,
|
||||
"total_flos": 43566831157248.0,
|
||||
"train_loss": 0.17250009640455247,
|
||||
"train_runtime": 4072.8546,
|
||||
"train_samples_per_second": 3.683,
|
||||
"train_steps_per_second": 0.46
|
||||
}
|
||||
1876
trainer_log.jsonl
Normal file
1876
trainer_log.jsonl
Normal file
File diff suppressed because it is too large
Load Diff
13168
trainer_state.json
Normal file
13168
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
training_loss.png
Normal file
BIN
training_loss.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
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