初始化项目，由ModelHub XC社区提供模型

Model: rizwan261/smollm2-135m-text2cypher
Source: Original Platform

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,107 @@
+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+datasets:
+  - RomanTeucher/text2cypher-curated
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text2cypher
+  - cypher
+  - neo4j
+  - lora
+---
+
+# SmolLM2-135M-text2cypher
+
+A fine-tune of [`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
+that turns a natural-language question + a graph schema into a **Cypher** query.
+Trained with LoRA on [`RomanTeucher/text2cypher-curated`](https://huggingface.co/datasets/RomanTeucher/text2cypher-curated)
+(1000 train / 75 val / 50 test); the adapters are **merged into the base weights**,
+so this is a standalone full model that loads like any HF checkpoint — no PEFT needed.
+**This model is a submission for an interview.** 
+
+## Usage
+
+The model expects the same ChatML prompt it was trained on: a fixed system message
+plus a user turn of `Schema:` + `Question:`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "rizwan261/smollm2-135m-text2cypher"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+SYSTEM = ("You are a text-to-Cypher engine for Neo4j. Given a graph schema and a "
+          "question, output a single valid Cypher query that answers the question. "
+          "Use only labels, relationship types and properties that appear in the "
+          "schema. Output the Cypher query and nothing else: no explanation, no "
+          "comments, no markdown code fences.")
+
+schema = "Graph schema: Relevant node labels and their properties (with datatypes) are:\nUpdateDate {update_date: DATE}"
+question = "Which nodes are connected to UpdateDate where update_date is 2008-01-29, and also to another node?"
+messages = [
+    {"role": "system", "content": SYSTEM},
+    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion:\n{question}"},
+]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
+out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+print(tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+
+```
+
+## Training
+
+LoRA (r=16, α=32, dropout=0.05) on the attention + MLP projections, completion-only
+loss (the prompt is masked, loss is on the target Cypher only), then merged.
+
+| | |
+|---|---|
+| epochs | 20 |
+| learning rate | 5e-5, cosine, 5% warmup |
+| effective batch | 16 (bs 4 × grad-accum 4) |
+| max length | 1024 |
+| best val loss | ~0.32 (by `eval_loss`) |
+
+## Evaluation
+
+Test split (n=50), greedy decoding. **Translation / structural metrics** over the
+whole split — surface proxies for correctness:
+
+| metric | base | this model |
+| --- | ---: | ---: |
+| exact_match | 0.00 | 0.26 |
+| structural_f1 | 0.18 | 0.81 |
+| google_bleu | 0.07 | 0.70 |
+| well_formed | 0.00 | 0.98 |
+
+**Execution-based** evaluation against the live Neo4j demo databases, on the 18/50
+samples that carry a database reference — predicted and gold queries are run and
+their *result sets* compared. This is the honest measure, and each stricter layer
+peels back the previous one's optimism:
+
+| how strict | metric | base | this model |
+| --- | --- | ---: | ---: |
+| real parser + live schema | CyVer KG-Valid-Query | 0.00 | 0.61 |
+| runs on the DB | query executes | 0.00 | 0.72 |
+| **returns the right rows** | exec ExactMatch (non-trivial) | 0.00 | **0.00** |
+
+The surface metrics jump, but execution accuracy stays at zero: the model writes
+well-formed, schema-valid, runnable Cypher that returns the *wrong data*.
+
+## Limitations
+
+A 135M model produces Cypher that often *looks* right but isn't: only ~26% are
+exact matches, and **non-trivial execution accuracy is ~0** — well-formed,
+runnable queries that return the wrong rows. It reliably gets node labels, clauses
+and the query skeleton, but struggles with same-node/co-reference patterns,
+multi-stage aggregation, and occasionally hallucinates functions. Execution
+numbers come from a small (n=18) subset run against live demo databases, so they
+can drift.
+
+## License
+
+Apache-2.0, inherited from the SmolLM2 base model.

chat_template.jinja

@@ -0,0 +1,6 @@
+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

@@ -0,0 +1,40 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_parameters": {
+    "rope_theta": 100000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "5.12.1",
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "5.12.1"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:532d759c2ac6bab59e737f61eadf8a2111f277bc8106eabb33a1be84c5d2d5d4
+size 269060552

tokenizer_config.json

@@ -0,0 +1,19 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

train_summary.json

@@ -0,0 +1,54 @@
+{
+  "config": {
+    "model_name": "HuggingFaceTB/SmolLM2-135M-Instruct",
+    "dataset_name": "RomanTeucher/text2cypher-curated",
+    "output_dir": "outputs/smollm2-135m-text2cypher",
+    "max_length": 1024,
+    "epochs": 20.0,
+    "learning_rate": 5e-05,
+    "weight_decay": 0.01,
+    "warmup_ratio": 0.05,
+    "warmup_steps": null,
+    "lr_scheduler_type": "cosine",
+    "train_batch_size": 4,
+    "eval_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "max_grad_norm": 1.0,
+    "bf16": null,
+    "fp16": null,
+    "use_lora": true,
+    "lora_r": 16,
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "logging_steps": 10,
+    "eval_strategy": "epoch",
+    "save_strategy": "epoch",
+    "save_total_limit": 1,
+    "load_best_model_at_end": true,
+    "metric_for_best_model": "eval_loss",
+    "greater_is_better": false,
+    "seed": 42,
+    "cpu_threads": null,
+    "push_to_hub": false,
+    "hub_model_id": null
+  },
+  "tokenization": {
+    "train": {
+      "n": 1000,
+      "truncated": 0,
+      "empty_target": 0
+    },
+    "val": {
+      "n": 75,
+      "truncated": 0,
+      "empty_target": 0
+    }
+  },
+  "final_eval": {
+    "eval_loss": 0.3248527944087982,
+    "eval_runtime": 2.1616,
+    "eval_samples_per_second": 34.696,
+    "eval_steps_per_second": 8.79,
+    "epoch": 20.0
+  }
+}