Upload 7B-L3

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- ESCAD/OpenRTLSet
+tags:
+- verilog
+- systemverilog
+- rtl
+- code
+---
+
+# verilog-coder (7B, LoRA)
+
+Qwen/Qwen2.5-Coder-7B-Instruct fine-tuned for Verilog/SystemVerilog generation from a natural-language
+spec (optionally with the module interface). Code, data pipeline, and evaluation harness:
+[https://github.com/Henil-08/verilog-coder-ft](https://github.com/Henil-08/verilog-coder-ft).
+
+## Training data
+
+[OpenRTLSet](https://huggingface.co/datasets/ESCAD/OpenRTLSet): 131k Verilog modules from
+GitHub, each paired with an LLM-written description. After dropping 54.6k fuzzer-generated
+modules, deduplicating, and capping examples at 2048 tokens, 53.7k remain; this run used
+40,000 of them (~93% Verilog, ~7% SystemVerilog). About 30% of
+training prompts describe the ports in prose instead of giving the header as code, which
+keeps the model usable when no header is provided.
+
+The raw corpus contains UVM testbench code in only 3 rows, and none of them survive
+filtering. This model was not trained or evaluated on UVM and makes no claim of UVM
+capability.
+
+## Training setup
+
+- Method: LoRA, 1 epoch, 29,906,052 supervised tokens
+- LoRA rank 64, alpha 128, dropout 0.05, targets: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- LR 0.0001 (cosine, 3% warmup), effective batch 64, max length 2048, bf16
+- HF Transformers + PEFT + DeepSpeed ZeRO-2 on 1× H100 (Modal);
+  79 min of training, ~$5.37
+- Loss on the code (assistant) tokens only; final eval loss 0.2653
+- Selected among LoRA/QLoRA/full-fine-tune configs by pass@1 on a validation split, so the
+  test numbers below were not used for model selection.
+
+## Evaluation (pass@1, greedy decoding)
+
+| Benchmark | Base | Fine-tuned | Items gained / lost |
+|---|---|---|---|
+| OpenRTLSet held-out (diff-sim) | 33.2% (131/394, 95% CI 28.8–38.0) | 46.5% (183/394, 95% CI 41.6–51.4) | +59 / -7 (McNemar p<0.001) |
+| VerilogEval v2 spec-to-RTL (published prompts) | 31.4% (49/156, 95% CI 24.6–39.1) | 23.7% (37/156, 95% CI 17.7–31.0) | +10 / -22 (McNemar p=0.050) |
+| VerilogEval v2 + interface code block | 38.5% (60/156, 95% CI 31.2–46.3) | 28.8% (45/156, 95% CI 22.3–36.4) | +9 / -24 (McNemar p=0.013) |
+
+- **OpenRTLSet held-out**: 394 standalone modules not seen in training. A generated module
+  passes if it matches the reference's ports and its outputs equal the reference's on every
+  half-cycle of 500 cycles of random stimulus (Verilator). Random stimulus is evidence of
+  equivalence, not proof.
+- **VerilogEval v2**: the official testbenches and Icarus Verilog v12 pass rule, with this
+  project's chat prompt (scores are not directly comparable to leaderboard numbers that use
+  other prompts).
+
+## Limitations
+
+- **On VerilogEval this fine-tune scores below its base model** (23.7% vs 31.4% with the published prompts). Fine-tuning on OpenRTLSet's real-world modules and LLM-written descriptions improved held-out OpenRTLSet modules but cost accuracy on VerilogEval's textbook-style problems; for that style of task the base model is the better choice.
+- Greedy decoding sometimes falls into repetition loops on long modules; about
+  13% of eval outputs hit the 2048-token cap.
+- Trained on single-module completions; multi-module designs and testbenches are out of
+  scope. Not evaluated for synthesis quality, timing, or area.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("henil08/verilog-coder-7b")
+model = AutoModelForCausalLM.from_pretrained("henil08/verilog-coder-7b", dtype="auto", device_map="auto")
+```
+
+Prompt format (system + user), as in training:
+
+```text
+Specification:
+<description>
+
+Module interface:
+```verilog
+module ...(...);
+```
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\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>" }}
+    {%- 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.20.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.20.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35e6b03404c6c96308f2e36fe47ae80f909d72c4515637732181414d9be33ce8
+size 15231272152

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}