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Model: ragraph-ai/stable-cypher-instruct-3b
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+metrics:
+- bleu
+- rouge
+tags:
+- causal-lm
+- code
+- cypher
+- graph
+- neo4j
+inference: false
+widget:
+- text: >-
+    Show me the people who have Python and Cloud skills and have been in the
+    company for at least 3 years.
+  example_title: Example 1
+- text: What is the IMDb rating of Pulp Fiction?
+  example_title: Example 2
+- text: >-
+    Display the first 3 users followed by 'Neo4j' who have more than 10000
+    followers.
+  example_title: Example 3
+base_model:
+- stabilityai/stable-code-instruct-3b
+base_model_relation: finetune
+---
+
+## Model Description
+
+A specialized 3B parameters model beating SOTA models such as GPT4-o at generating CYPHER.
+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.
+
+## Usage
+
+### Safetensors (recommended)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("ragraph-ai/stable-cypher-instruct-3b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("ragraph-ai/stable-cypher-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years."
+    }
+]
+
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+tokens = model.generate(
+        **inputs,
+        max_new_tokens=128,
+        do_sample=True,
+        top_p=0.9,
+        temperature=0.2,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+outputs = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0]
+```
+
+### GGUF
+
+```python
+from llama_cpp import Llama
+
+# Load the GGUF model
+print("Loading model...")
+model = Llama(
+    model_path=r"C:\Users\John\stable-cypher-instruct-3b.Q4_K_M.gguf",
+    n_ctx=512,
+    n_batch=512,
+    n_gpu_layers=-1,  # Use all available GPU layers
+    max_tokens=128,
+    top_p=0.9,
+    temperature=0.2,
+    verbose=False 
+)
+
+# Define your question
+question = "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years."
+
+# Create the full prompt (simulating the apply_chat_template function)
+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"
+
+# Generate response
+print("Generating response...")
+response = model(
+    full_prompt,
+    max_tokens=128,
+    stop=["<|im_end|>", "<|im_start|>"],
+    echo=False
+)
+
+# Extract and print the generated response
+answer = response['choices'][0]['text'].strip()
+print("\nQuestion:", question)
+print("\nGenerated Cypher statement:")
+print(answer)
+```
+
+## Performance
+
+| Metric       | stable-code-instruct-3b |   gpt4-o   | stable-cypher-instruct-3b |
+| :----------: | :---------------------: | :--------: | :-----------------------: |
+| BLEU-4       |         19.07           |   32.35    |         **88.63**         |
+| ROUGE-1      |         39.49           |   69.17    |         **95.09**         |
+| ROUGE-2      |         24.82           |   46.97    |         **90.71**         |
+| ROUGE-L      |         29.63           |   65.24    |         **91.51**         |
+| Jaro-Winkler |         52.21           |   86.38    |         **95.69**         |
+| Jaccard      |         25.55           |   72.80    |         **90.78**         |
+| Pass@1       |         0.00            |   0.00     |         **51.80**         |
+
+### Example
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6504bb76423b46492e7f38c7/pweL4qgmFaknLBYp-CGHm.png)
+
+### Eval params
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6504bb76423b46492e7f38c7/AT80-09XrHNz-dJs9TH3M.png)
+
+## Reproducability
+
+This is the config file from Llama Factory :
+
+```json
+{
+  "top.model_name": "Custom",
+  "top.finetuning_type": "lora",
+  "top.adapter_path": [],
+  "top.quantization_bit": "none",
+  "top.template": "default",
+  "top.rope_scaling": "none",
+  "top.booster": "none",
+  "train.training_stage": "Supervised Fine-Tuning",
+  "train.dataset_dir": "data",
+  "train.dataset": [
+    "cypher_opus"
+  ],
+  "train.learning_rate": "2e-4",
+  "train.num_train_epochs": "5.0",
+  "train.max_grad_norm": "1.0",
+  "train.max_samples": "5000",
+  "train.compute_type": "fp16",
+  "train.cutoff_len": 256,
+  "train.batch_size": 16,
+  "train.gradient_accumulation_steps": 2,
+  "train.val_size": 0.1,
+  "train.lr_scheduler_type": "cosine",
+  "train.logging_steps": 10,
+  "train.save_steps": 100,
+  "train.warmup_steps": 20,
+  "train.neftune_alpha": 0,
+  "train.optim": "adamw_torch",
+  "train.resize_vocab": false,
+  "train.packing": false,
+  "train.upcast_layernorm": false,
+  "train.use_llama_pro": false,
+  "train.shift_attn": false,
+  "train.report_to": false,
+  "train.num_layer_trainable": 3,
+  "train.name_module_trainable": "all",
+  "train.lora_rank": 64,
+  "train.lora_alpha": 64,
+  "train.lora_dropout": 0.1,
+  "train.loraplus_lr_ratio": 0,
+  "train.create_new_adapter": false,
+  "train.use_rslora": false,
+  "train.use_dora": true,
+  "train.lora_target": "",
+  "train.additional_target": "",
+  "train.dpo_beta": 0.1,
+  "train.dpo_ftx": 0,
+  "train.orpo_beta": 0.1,
+  "train.reward_model": null,
+  "train.use_galore": false,
+  "train.galore_rank": 16,
+  "train.galore_update_interval": 200,
+  "train.galore_scale": 0.25,
+  "train.galore_target": "all"
+}
+```
+
+I used llama.cpp to merge the LoRa and generate the quants.
+
+The progress achieved from the base model is significant but you will still need to finetune on your company's syntax and entities.
+I've been tickering with the training parameters for a few batches of training but there is room for improvements.
+I'm open to the idea of making a full tutorial if there is enough interest in this project.

config.json

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+{
+  "_name_or_path": "lakkeo/stable-cypher-instruct-3b",
+  "architectures": [
+    "StableLmForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 16384,
+  "model_type": "stablelm",
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+  "num_key_value_heads": 32,
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+    "_load_in_8bit": true,
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+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "fp4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
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+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": false,
+    "load_in_8bit": true,
+    "quant_method": "bitsandbytes"
+  },
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": false,
+  "use_parallel_residual": false,
+  "use_qkv_bias": false,
+  "vocab_size": 50304
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 0,
+  "transformers_version": "4.41.2"
+}

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special_tokens_map.json

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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  }
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50295": {
+      "content": "<repo_continuation>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50296": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50297": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50298": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'Create a Cypher statement to answer the following question:' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<|im_start|>system\n' + system_message + '<|im_end|>\n'}}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 4096,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "unk_token": "<|endoftext|>"
+}