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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- bleu |
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- rouge |
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tags: |
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- causal-lm |
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- code |
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- cypher |
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- graph |
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- neo4j |
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inference: false |
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widget: |
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- text: >- |
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Show me the people who have Python and Cloud skills and have been in the |
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company for at least 3 years. |
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example_title: Example 1 |
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- text: What is the IMDb rating of Pulp Fiction? |
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example_title: Example 2 |
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- text: >- |
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Display the first 3 users followed by 'Neo4j' who have more than 10000 |
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followers. |
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example_title: Example 3 |
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base_model: |
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- stabilityai/stable-code-instruct-3b |
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base_model_relation: finetune |
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--- |
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## Model Description |
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A specialized 3B parameters model beating SOTA models such as GPT4-o at generating CYPHER. |
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It's a finetune of https://huggingface.co/stabilityai/stable-code-instruct-3b trained on https://github.com/neo4j-labs/text2cypher/tree/main/datasets/synthetic_opus_demodbs to generate CYPHER queries from text to query GraphDB such as neo4j. |
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## Usage |
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### Safetensors (recommended) |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("lakkeo/stable-cypher-instruct-3b", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("lakkeo/stable-cypher-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True) |
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messages = [ |
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{ |
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"role": "user", |
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"content": "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years." |
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} |
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] |
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) |
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inputs = tokenizer([prompt], return_tensors="pt").to(model.device) |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=128, |
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do_sample=True, |
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top_p=0.9, |
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temperature=0.2, |
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pad_token_id=tokenizer.eos_token_id, |
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) |
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outputs = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0] |
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``` |
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### GGUF |
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```python |
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from llama_cpp import Llama |
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# Load the GGUF model |
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print("Loading model...") |
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model = Llama( |
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model_path=r"C:\Users\John\stable-cypher-instruct-3b.Q4_K_M.gguf", |
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n_ctx=512, |
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n_batch=512, |
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n_gpu_layers=-1, # Use all available GPU layers |
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max_tokens=128, |
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top_p=0.9, |
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temperature=0.2, |
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verbose=False |
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) |
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# Define your question |
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question = "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years." |
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# Create the full prompt (simulating the apply_chat_template function) |
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full_prompt = f"<|im_start|>system\nCreate a Cypher statement to answer the following question:<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n" |
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# Generate response |
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print("Generating response...") |
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response = model( |
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full_prompt, |
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max_tokens=128, |
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stop=["<|im_end|>", "<|im_start|>"], |
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echo=False |
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) |
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# Extract and print the generated response |
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answer = response['choices'][0]['text'].strip() |
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print("\nQuestion:", question) |
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print("\nGenerated Cypher statement:") |
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print(answer) |
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``` |
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## Performance |
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| Metric | stable-code-instruct-3b | gpt4-o | stable-cypher-instruct-3b | |
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| :----------: | :---------------------: | :--------: | :-----------------------: | |
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| BLEU-4 | 19.07 | 32.35 | **88.63** | |
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| ROUGE-1 | 39.49 | 69.17 | **95.09** | |
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| ROUGE-2 | 24.82 | 46.97 | **90.71** | |
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| ROUGE-L | 29.63 | 65.24 | **91.51** | |
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| Jaro-Winkler | 52.21 | 86.38 | **95.69** | |
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| Jaccard | 25.55 | 72.80 | **90.78** | |
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| Pass@1 | 0.00 | 0.00 | **51.80** | |
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### Example |
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### Eval params |
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## Reproducability |
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This is the config file from Llama Factory : |
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```json |
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{ |
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"top.model_name": "Custom", |
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"top.finetuning_type": "lora", |
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"top.adapter_path": [], |
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"top.quantization_bit": "none", |
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"top.template": "default", |
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"top.rope_scaling": "none", |
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"top.booster": "none", |
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"train.training_stage": "Supervised Fine-Tuning", |
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"train.dataset_dir": "data", |
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"train.dataset": [ |
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"cypher_opus" |
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], |
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"train.learning_rate": "2e-4", |
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"train.num_train_epochs": "5.0", |
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"train.max_grad_norm": "1.0", |
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"train.max_samples": "5000", |
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"train.compute_type": "fp16", |
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"train.cutoff_len": 256, |
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"train.batch_size": 16, |
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"train.gradient_accumulation_steps": 2, |
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"train.val_size": 0.1, |
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"train.lr_scheduler_type": "cosine", |
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"train.logging_steps": 10, |
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"train.save_steps": 100, |
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"train.warmup_steps": 20, |
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"train.neftune_alpha": 0, |
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"train.optim": "adamw_torch", |
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"train.resize_vocab": false, |
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"train.packing": false, |
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"train.upcast_layernorm": false, |
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"train.use_llama_pro": false, |
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"train.shift_attn": false, |
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"train.report_to": false, |
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"train.num_layer_trainable": 3, |
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"train.name_module_trainable": "all", |
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"train.lora_rank": 64, |
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"train.lora_alpha": 64, |
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"train.lora_dropout": 0.1, |
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"train.loraplus_lr_ratio": 0, |
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"train.create_new_adapter": false, |
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"train.use_rslora": false, |
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"train.use_dora": true, |
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"train.lora_target": "", |
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"train.additional_target": "", |
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"train.dpo_beta": 0.1, |
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"train.dpo_ftx": 0, |
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"train.orpo_beta": 0.1, |
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"train.reward_model": null, |
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"train.use_galore": false, |
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"train.galore_rank": 16, |
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"train.galore_update_interval": 200, |
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"train.galore_scale": 0.25, |
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"train.galore_target": "all" |
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} |
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``` |
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I used llama.cpp to merge the LoRa and generate the quants. |
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The progress achieved from the base model is significant but you will still need to finetune on your company's syntax and entities. |
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I've been tickering with the training parameters for a few batches of training but there is room for improvements. |
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I'm open to the idea of making a full tutorial if there is enough interest in this project. |
