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Model: sarvan-2187/qrious-code-1.0 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/Qwen3-1.7B
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tags:
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- qiskit
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- quantum-computing
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- code-generation
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- lora
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- qlora
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- unsloth
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language:
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- en
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pipeline_tag: text-generation
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---
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<div align="center">
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</div>
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# qrious-code-1.0
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A Qiskit coding assistant: [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) fine-tuned with QLoRA on quantum-computing code/QA data, designed to be paired with a retrieval-augmented generation (RAG) layer over the official Qiskit documentation.
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This repository hosts the **merged** model. The LoRA adapter has been merged into the base weights and saved in standard fp16, so it loads directly with `AutoModelForCausalLM.from_pretrained`, no separate adapter step needed.
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## Model Details
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- **Base model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
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- **Fine-tuning method:** QLoRA (4-bit NF4 base, LoRA r=16/alpha=16 on `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`), merged into fp16 for this release
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- **Trainable params during fine-tuning:** 17.4M / 1.74B (1.00%)
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- **Training framework:** [Unsloth](https://github.com/unslothai/unsloth) + `trl.SFTTrainer`
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- **Hardware:** single RTX 3050 Laptop GPU (6GB VRAM)
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- **License:** Apache 2.0 (inherited from the base model)
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## Training Data
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Fine-tuned on a cleaned subset of [`samuellimabraz/quantum-assistant`](https://huggingface.co/datasets/samuellimabraz/quantum-assistant):
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| split | raw rows | cleaned rows |
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|---|---|---|
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| train | 5837 | 4168 |
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| val | 1239 | 887 |
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| test | 1290 | 925 |
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Cleaning removed image-dependent QA rows (hand-verified that the large majority genuinely depend on the image), de-duplicated near-identical questions, and filtered rows exceeding the 1024-token training context.
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## Training Configuration
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- 3 epochs, batch size 2 × gradient accumulation 8, packed 1024-token sequences (195 steps total)
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- Optimizer `paged_adamw_8bit`, cosine LR schedule, peak LR 2e-4
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- Train loss 2.70 → 0.77 (avg 0.989); eval loss 0.896 → 0.819 → 0.809 across 3 epochs (still improving, no overfitting divergence)
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- Wall time: 48 minutes
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## Evaluation
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Scored on held-out test rows (unseen during training), tuned adapter vs. the un-tuned base model:
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| Metric | Base | Tuned | Δ |
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|---|---|---|---|
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| Code pass@1 (744 code rows, executed in a sandbox against ground-truth tests) | 10.2% | 25.0% | **+14.8 pts** |
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| QA judge score /5 (136 QA rows, judged by base Qwen3-1.7B) | 2.21 | 2.81 | **+0.60** |
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| Instruction-following compliance | n/a | 1.0 | fixed 5-prompt probe set |
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Code pass@1 improved in every one of 4 evaluation shards. The QA judge score improved in 3/4 shards, reported as-is rather than smoothed, since self-judging by a same-size base model is a known weak signal.
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## Retrieval-Augmented Generation (companion, not baked into these weights)
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This model is designed to run behind a RAG layer over the official [Qiskit/documentation](https://github.com/Qiskit/documentation) corpus (chunked, embedded with `BAAI/bge-small-en-v1.5`, reranked with `cross-encoder/ms-marco-MiniLM-L-6-v2`, indexed in Qdrant). In side-by-side testing, RAG grounding measurably reduces hallucination of deprecated/removed Qiskit APIs (`qiskit.opflow`, `qiskit_aqua`, `IBMQ.load_account()`, etc.), though it isn't perfect: the model can still recite deprecated syntax when retrieval doesn't surface the right page for a given query. The RAG pipeline itself is not part of this repository's weights; see [github.com/sarvan-2187/qrious-code-1.0](https://github.com/sarvan-2187/qrious-code-1.0) for the full serving code.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "sarvan-2187/qrious-code-1.0"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")
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messages = [
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{"role": "system", "content": "You are a Qiskit coding assistant."},
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{"role": "user", "content": "How do I create a Bell state in Qiskit?"},
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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> Pass `enable_thinking=False` in the chat template: Qwen3's default reasoning mode otherwise spends the generation budget on unrequested `<think>` output before answering.
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## Limitations
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- QA-answer quality improved less clearly than code-generation correctness (self-judged by a same-size base model, a known weak evaluation signal).
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- Can still recite deprecated Qiskit APIs when used without RAG grounding, or when RAG retrieval doesn't surface the right context for a query.
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- Trained/evaluated at a 1024-token context; serving at a 4096-token context works (verified) but wasn't part of the original training distribution.
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## Citation
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Not a formal research release, a personal project. If referencing it: base model [Qwen3](https://huggingface.co/Qwen/Qwen3-1.7B), dataset [`samuellimabraz/quantum-assistant`](https://huggingface.co/datasets/samuellimabraz/quantum-assistant), fine-tuned with [Unsloth](https://github.com/unslothai/unsloth).
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89
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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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- 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>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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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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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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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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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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63
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": "float16",
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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": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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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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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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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": 1000000,
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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": "5.5.0",
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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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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": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.5.0"
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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:c569ac72229f706c81eebb89db2eec7c746499ec310fdea6fe83ef4a737df475
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size 3441185296
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1
output-reports/README.md
Normal file
1
output-reports/README.md
Normal file
@@ -0,0 +1 @@
|
|||||||
|
## This folder contains the reports of testing dataset
|
||||||
51
output-reports/eval_report_v1_combined.json
Normal file
51
output-reports/eval_report_v1_combined.json
Normal file
@@ -0,0 +1,51 @@
|
|||||||
|
{
|
||||||
|
"total_code_rows": 744,
|
||||||
|
"total_qa_rows": 136,
|
||||||
|
"code_pass_at_1": {
|
||||||
|
"base_overall": 0.102,
|
||||||
|
"tuned_overall": 0.25,
|
||||||
|
"improvement": 0.148,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 0.114,
|
||||||
|
"circuits_and_gates": 0.125,
|
||||||
|
"hardware_and_providers": 0.133,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.049,
|
||||||
|
"quantum_info_and_operators": 0.098,
|
||||||
|
"transpilation_and_compilation": 0.04
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 0.125,
|
||||||
|
"circuits_and_gates": 0.319,
|
||||||
|
"hardware_and_providers": 0.169,
|
||||||
|
"noise_and_error_mitigation": 0.113,
|
||||||
|
"primitives_and_execution": 0.065,
|
||||||
|
"quantum_info_and_operators": 0.266,
|
||||||
|
"transpilation_and_compilation": 0.242
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"qa_judge_score_1to5": {
|
||||||
|
"base_overall": 2.208,
|
||||||
|
"tuned_overall": 2.808,
|
||||||
|
"improvement": 0.6,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 1.833,
|
||||||
|
"circuits_and_gates": 1.922,
|
||||||
|
"hardware_and_providers": 2.593,
|
||||||
|
"noise_and_error_mitigation": 2.617,
|
||||||
|
"primitives_and_execution": 2.25,
|
||||||
|
"quantum_info_and_operators": 2.08,
|
||||||
|
"transpilation_and_compilation": 2.46
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 1.875,
|
||||||
|
"circuits_and_gates": 2.792,
|
||||||
|
"hardware_and_providers": 3.833,
|
||||||
|
"noise_and_error_mitigation": 1.7,
|
||||||
|
"primitives_and_execution": 3.333,
|
||||||
|
"quantum_info_and_operators": 2.688,
|
||||||
|
"transpilation_and_compilation": 3.19
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"instruction_following_compliance_rate": 1.0
|
||||||
|
}
|
||||||
80
output-reports/eval_report_v1_shard1of4.json
Normal file
80
output-reports/eval_report_v1_shard1of4.json
Normal file
@@ -0,0 +1,80 @@
|
|||||||
|
{
|
||||||
|
"shard": "1/4",
|
||||||
|
"n_code_rows": 186,
|
||||||
|
"n_qa_rows": 34,
|
||||||
|
"code_pass_at_1": {
|
||||||
|
"base_overall": 0.108,
|
||||||
|
"tuned_overall": 0.231,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 0.08,
|
||||||
|
"circuits_and_gates": 0.118,
|
||||||
|
"hardware_and_providers": 0.333,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.067,
|
||||||
|
"quantum_info_and_operators": 0.154,
|
||||||
|
"transpilation_and_compilation": 0.053
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 0.16,
|
||||||
|
"circuits_and_gates": 0.204,
|
||||||
|
"hardware_and_providers": 0.333,
|
||||||
|
"noise_and_error_mitigation": 0.2,
|
||||||
|
"primitives_and_execution": 0.133,
|
||||||
|
"quantum_info_and_operators": 0.423,
|
||||||
|
"transpilation_and_compilation": 0.263
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"qa_judge_score_1to5": {
|
||||||
|
"base_overall": 1.94,
|
||||||
|
"tuned_overall": 2.76,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 1.0,
|
||||||
|
"circuits_and_gates": 1.44,
|
||||||
|
"hardware_and_providers": 2.12,
|
||||||
|
"noise_and_error_mitigation": 2.8,
|
||||||
|
"primitives_and_execution": 1.0,
|
||||||
|
"quantum_info_and_operators": 3.0,
|
||||||
|
"transpilation_and_compilation": 2.2
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 2.0,
|
||||||
|
"circuits_and_gates": 2.44,
|
||||||
|
"hardware_and_providers": 3.0,
|
||||||
|
"noise_and_error_mitigation": 1.8,
|
||||||
|
"primitives_and_execution": 5.0,
|
||||||
|
"quantum_info_and_operators": 3.0,
|
||||||
|
"transpilation_and_compilation": 4.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"instruction_following": {
|
||||||
|
"compliance_rate": 1.0,
|
||||||
|
"probes": [
|
||||||
|
{
|
||||||
|
"prompt": "Write only the Qiskit code to create a 2-qubit Bell state circuit. Return only code, no explanation.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\n# Create a 2-qubit circuit\nqc = QuantumCircuit(2, 2)\n\n# Create Bell state (entangled state)\nqc.h(0)\nqc.cx(0, 1)\n\n# Print the circuit\nprint(qc)\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Return only a Python function `ghz_state(n)` that builds an n-qubit GHZ state circuit using Qiskit. No prose, code only.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\ndef ghz_state(n):\n qc = QuantumCircuit(n)\n for i in range(n):\n qc.h(i)\n return qc\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Give me just the Qiskit import statement for QuantumCircuit. One line, nothing else.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Output only the code for a QFT circuit on 3 qubits using qiskit.circuit.library.QFT. No commentary.",
|
||||||
|
"response": "from qiskit import QuantumCircuit\nfrom qiskit.circuit.library import QFT\n\nqc = QuantumCircuit(3)\nQFT.qft(qc, 3)",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Respond with only the code that transpiles a circuit `qc` for backend `backend` at optimization_level=3. No explanation text.",
|
||||||
|
"response": "```python\nfrom qiskit import transpilation\nfrom qiskit.transpilation import transpile\nfrom qiskit.transpilation.optimization import OptimizationLevel\n\n# Transpile the circuit for the specified backend with optimization level 3\ntranspiled_qc = transpile(qc, backend, optimization_level=3)\n```",
|
||||||
|
"compliant": true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"note": "RAG-on/off hallucination check skipped - deferred to Phase 6 (needs the RAG pipeline)."
|
||||||
|
}
|
||||||
80
output-reports/eval_report_v1_shard2of4.json
Normal file
80
output-reports/eval_report_v1_shard2of4.json
Normal file
@@ -0,0 +1,80 @@
|
|||||||
|
{
|
||||||
|
"shard": "2/4",
|
||||||
|
"n_code_rows": 186,
|
||||||
|
"n_qa_rows": 34,
|
||||||
|
"code_pass_at_1": {
|
||||||
|
"base_overall": 0.124,
|
||||||
|
"tuned_overall": 0.306,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 0.217,
|
||||||
|
"circuits_and_gates": 0.124,
|
||||||
|
"hardware_and_providers": 0.2,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.056,
|
||||||
|
"quantum_info_and_operators": 0.143,
|
||||||
|
"transpilation_and_compilation": 0.0
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 0.13,
|
||||||
|
"circuits_and_gates": 0.433,
|
||||||
|
"hardware_and_providers": 0.2,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.056,
|
||||||
|
"quantum_info_and_operators": 0.286,
|
||||||
|
"transpilation_and_compilation": 0.167
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"qa_judge_score_1to5": {
|
||||||
|
"base_overall": 2.06,
|
||||||
|
"tuned_overall": 2.85,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 2.5,
|
||||||
|
"circuits_and_gates": 1.67,
|
||||||
|
"hardware_and_providers": 3.25,
|
||||||
|
"noise_and_error_mitigation": 1.0,
|
||||||
|
"primitives_and_execution": 3.0,
|
||||||
|
"quantum_info_and_operators": 1.5,
|
||||||
|
"transpilation_and_compilation": 2.14
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 2.5,
|
||||||
|
"circuits_and_gates": 2.67,
|
||||||
|
"hardware_and_providers": 5.0,
|
||||||
|
"noise_and_error_mitigation": 1.0,
|
||||||
|
"primitives_and_execution": 3.0,
|
||||||
|
"quantum_info_and_operators": 2.25,
|
||||||
|
"transpilation_and_compilation": 3.29
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"instruction_following": {
|
||||||
|
"compliance_rate": 1.0,
|
||||||
|
"probes": [
|
||||||
|
{
|
||||||
|
"prompt": "Write only the Qiskit code to create a 2-qubit Bell state circuit. Return only code, no explanation.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\n# Create a 2-qubit circuit\nqc = QuantumCircuit(2, 2)\n\n# Create Bell state (entangled state)\nqc.h(0)\nqc.cx(0, 1)\n\n# Print the circuit\nprint(qc)\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Return only a Python function `ghz_state(n)` that builds an n-qubit GHZ state circuit using Qiskit. No prose, code only.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\ndef ghz_state(n):\n qc = QuantumCircuit(n)\n for i in range(n):\n qc.h(i)\n return qc\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Give me just the Qiskit import statement for QuantumCircuit. One line, nothing else.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Output only the code for a QFT circuit on 3 qubits using qiskit.circuit.library.QFT. No commentary.",
|
||||||
|
"response": "from qiskit import QuantumCircuit\nfrom qiskit.circuit.library import QFT\n\nqc = QuantumCircuit(3)\nQFT.qft(qc, 3)",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Respond with only the code that transpiles a circuit `qc` for backend `backend` at optimization_level=3. No explanation text.",
|
||||||
|
"response": "```python\nfrom qiskit import transpilation\nfrom qiskit.transpilation import transpile\nfrom qiskit.transpilation.optimization import OptimizationLevel\n\n# Transpile the circuit for the specified backend with optimization level 3\ntranspiled_qc = transpile(qc, backend, optimization_level=3)\n```",
|
||||||
|
"compliant": true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"note": "RAG-on/off hallucination check skipped - deferred to Phase 6 (needs the RAG pipeline)."
|
||||||
|
}
|
||||||
80
output-reports/eval_report_v1_shard3of4.json
Normal file
80
output-reports/eval_report_v1_shard3of4.json
Normal file
@@ -0,0 +1,80 @@
|
|||||||
|
{
|
||||||
|
"shard": "3/4",
|
||||||
|
"n_code_rows": 186,
|
||||||
|
"n_qa_rows": 34,
|
||||||
|
"code_pass_at_1": {
|
||||||
|
"base_overall": 0.108,
|
||||||
|
"tuned_overall": 0.22,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 0.158,
|
||||||
|
"circuits_and_gates": 0.132,
|
||||||
|
"hardware_and_providers": 0.0,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.071,
|
||||||
|
"quantum_info_and_operators": 0.062,
|
||||||
|
"transpilation_and_compilation": 0.105
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 0.211,
|
||||||
|
"circuits_and_gates": 0.275,
|
||||||
|
"hardware_and_providers": 0.143,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.071,
|
||||||
|
"quantum_info_and_operators": 0.156,
|
||||||
|
"transpilation_and_compilation": 0.263
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"qa_judge_score_1to5": {
|
||||||
|
"base_overall": 1.68,
|
||||||
|
"tuned_overall": 2.56,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 2.33,
|
||||||
|
"circuits_and_gates": 1.58,
|
||||||
|
"hardware_and_providers": 1.0,
|
||||||
|
"noise_and_error_mitigation": 1.67,
|
||||||
|
"primitives_and_execution": 2.0,
|
||||||
|
"quantum_info_and_operators": 1.25,
|
||||||
|
"transpilation_and_compilation": 2.0
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 1.0,
|
||||||
|
"circuits_and_gates": 3.17,
|
||||||
|
"hardware_and_providers": 2.33,
|
||||||
|
"noise_and_error_mitigation": 2.0,
|
||||||
|
"primitives_and_execution": 2.33,
|
||||||
|
"quantum_info_and_operators": 2.5,
|
||||||
|
"transpilation_and_compilation": 2.67
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"instruction_following": {
|
||||||
|
"compliance_rate": 1.0,
|
||||||
|
"probes": [
|
||||||
|
{
|
||||||
|
"prompt": "Write only the Qiskit code to create a 2-qubit Bell state circuit. Return only code, no explanation.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\n# Create a 2-qubit circuit\nqc = QuantumCircuit(2, 2)\n\n# Create Bell state (entangled state)\nqc.h(0)\nqc.cx(0, 1)\n\n# Print the circuit\nprint(qc)\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Return only a Python function `ghz_state(n)` that builds an n-qubit GHZ state circuit using Qiskit. No prose, code only.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\ndef ghz_state(n):\n qc = QuantumCircuit(n)\n for i in range(n):\n qc.h(i)\n return qc\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Give me just the Qiskit import statement for QuantumCircuit. One line, nothing else.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Output only the code for a QFT circuit on 3 qubits using qiskit.circuit.library.QFT. No commentary.",
|
||||||
|
"response": "from qiskit import QuantumCircuit\nfrom qiskit.circuit.library import QFT\n\nqc = QuantumCircuit(3)\nQFT.qft(qc, 3)",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Respond with only the code that transpiles a circuit `qc` for backend `backend` at optimization_level=3. No explanation text.",
|
||||||
|
"response": "```python\nfrom qiskit import transpilation\nfrom qiskit.transpilation import transpile\nfrom qiskit.transpilation.optimization import OptimizationLevel\n\n# Transpile the circuit for the specified backend with optimization level 3\ntranspiled_qc = transpile(qc, backend, optimization_level=3)\n```",
|
||||||
|
"compliant": true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"note": "RAG-on/off hallucination check skipped - deferred to Phase 6 (needs the RAG pipeline)."
|
||||||
|
}
|
||||||
80
output-reports/eval_report_v1_shard4of4.json
Normal file
80
output-reports/eval_report_v1_shard4of4.json
Normal file
@@ -0,0 +1,80 @@
|
|||||||
|
{
|
||||||
|
"shard": "4/4",
|
||||||
|
"n_code_rows": 186,
|
||||||
|
"n_qa_rows": 34,
|
||||||
|
"code_pass_at_1": {
|
||||||
|
"base_overall": 0.07,
|
||||||
|
"tuned_overall": 0.242,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 0.0,
|
||||||
|
"circuits_and_gates": 0.125,
|
||||||
|
"hardware_and_providers": 0.0,
|
||||||
|
"noise_and_error_mitigation": 0.0,
|
||||||
|
"primitives_and_execution": 0.0,
|
||||||
|
"quantum_info_and_operators": 0.033,
|
||||||
|
"transpilation_and_compilation": 0.0
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 0.0,
|
||||||
|
"circuits_and_gates": 0.365,
|
||||||
|
"hardware_and_providers": 0.0,
|
||||||
|
"noise_and_error_mitigation": 0.25,
|
||||||
|
"primitives_and_execution": 0.0,
|
||||||
|
"quantum_info_and_operators": 0.2,
|
||||||
|
"transpilation_and_compilation": 0.273
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"qa_judge_score_1to5": {
|
||||||
|
"base_overall": 3.15,
|
||||||
|
"tuned_overall": 3.06,
|
||||||
|
"base_by_category": {
|
||||||
|
"algorithms_and_applications": 1.5,
|
||||||
|
"circuits_and_gates": 3.0,
|
||||||
|
"hardware_and_providers": 4.0,
|
||||||
|
"noise_and_error_mitigation": 5.0,
|
||||||
|
"primitives_and_execution": 3.0,
|
||||||
|
"quantum_info_and_operators": 2.57,
|
||||||
|
"transpilation_and_compilation": 3.5
|
||||||
|
},
|
||||||
|
"tuned_by_category": {
|
||||||
|
"algorithms_and_applications": 2.0,
|
||||||
|
"circuits_and_gates": 2.89,
|
||||||
|
"hardware_and_providers": 5.0,
|
||||||
|
"noise_and_error_mitigation": 2.0,
|
||||||
|
"primitives_and_execution": 3.0,
|
||||||
|
"quantum_info_and_operators": 3.0,
|
||||||
|
"transpilation_and_compilation": 2.8
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"instruction_following": {
|
||||||
|
"compliance_rate": 1.0,
|
||||||
|
"probes": [
|
||||||
|
{
|
||||||
|
"prompt": "Write only the Qiskit code to create a 2-qubit Bell state circuit. Return only code, no explanation.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\n# Create a 2-qubit circuit\nqc = QuantumCircuit(2, 2)\n\n# Create Bell state (entangled state)\nqc.h(0)\nqc.cx(0, 1)\n\n# Print the circuit\nprint(qc)\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Return only a Python function `ghz_state(n)` that builds an n-qubit GHZ state circuit using Qiskit. No prose, code only.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.quantum_info import Statevector\n\ndef ghz_state(n):\n qc = QuantumCircuit(n)\n for i in range(n):\n qc.h(i)\n return qc\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Give me just the Qiskit import statement for QuantumCircuit. One line, nothing else.",
|
||||||
|
"response": "```python\nfrom qiskit import QuantumCircuit\n```",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Output only the code for a QFT circuit on 3 qubits using qiskit.circuit.library.QFT. No commentary.",
|
||||||
|
"response": "from qiskit import QuantumCircuit\nfrom qiskit.circuit.library import QFT\n\nqc = QuantumCircuit(3)\nQFT.qft(qc, 3)",
|
||||||
|
"compliant": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"prompt": "Respond with only the code that transpiles a circuit `qc` for backend `backend` at optimization_level=3. No explanation text.",
|
||||||
|
"response": "```python\nfrom qiskit import transpilation\nfrom qiskit.transpilation import transpile\nfrom qiskit.transpilation.optimization import OptimizationLevel\n\n# Transpile the circuit for the specified backend with optimization level 3\ntranspiled_qc = transpile(qc, backend, optimization_level=3)\n```",
|
||||||
|
"compliant": true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"note": "RAG-on/off hallucination check skipped - deferred to Phase 6 (needs the RAG pipeline)."
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||||
|
size 11422650
|
||||||
29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
||||||
|
"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,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user