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Model: chatdb/natural-sql-7b Source: Original Platform
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README.md
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README.md
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---
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base_model: deepseek-ai/deepseek-coder-6.7b-instruct
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tags:
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- instruct
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- finetune
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library_name: transformers
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license: cc-by-sa-4.0
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pipeline_tag: text-generation
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---
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# **Natural-SQL-7B by ChatDB**
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## Natural-SQL-7B is a model with very strong performance in Text-to-SQL instructions, has an excellent understanding of complex questions, and outperforms models of the same size in its space.
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<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/hafdsfrFCqrVbATIzV_EN.png" width="600">
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[ChatDB.ai](https://chatdb.ai) | [Notebook](https://github.com/cfahlgren1/natural-sql/blob/main/natural-sql-7b.ipynb) | [Twitter](https://twitter.com/calebfahlgren)
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# **Benchmarks**
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### *Results on Novel Datasets not trained on via SQL-Eval*
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<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/5ynfoKPzI3_-WasQQt7qR.png" width="800">
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<em>Big thanks to the [defog](https://huggingface.co/defog) team for open sourcing [sql-eval](https://github.com/defog-ai/sql-eval)</em>👏
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Natural-SQL also can handle complex, compound questions that other models typically struggle with. There is a more detailed writeup Here is a write up, small test done [here](https://chatdb.ai/post/naturalsql-vs-sqlcoder-for-text-to-sql).
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# Usage
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Make sure you have the correct version of the transformers library installed:
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```sh
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pip install transformers==4.35.2
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```
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### Loading the Model
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Use the following Python code to load the model:
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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("chatdb/natural-sql-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"chatdb/natural-sql-7b",
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device_map="auto",
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torch_dtype=torch.float16,
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)
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```
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### **License**
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The model weights are licensed under `CC BY-SA 4.0`, with extra guidelines for responsible use expanded from the original model's [Deepseek](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) license.
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You're free to use and adapt the model, even commercially.
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If you alter the weights, such as through fine-tuning, you must publicly share your changes under the same `CC BY-SA 4.0` license.
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### Generating SQL
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```python
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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generated_ids = model.generate(
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**inputs,
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num_return_sequences=1,
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eos_token_id=100001,
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pad_token_id=100001,
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max_new_tokens=400,
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do_sample=False,
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num_beams=1,
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)
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outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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print(outputs[0].split("```sql")[-1])
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```
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# Prompt Template
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```
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# Task
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Generate a SQL query to answer the following question: `{natural language question}`
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### PostgreSQL Database Schema
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The query will run on a database with the following schema:
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<SQL Table DDL Statements>
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# SQL
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Here is the SQL query that answers the question: `{natural language question}`
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'''sql
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```
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# Example SQL Output
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### Example Schemas
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```sql
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CREATE TABLE users (
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user_id SERIAL PRIMARY KEY,
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username VARCHAR(50) NOT NULL,
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email VARCHAR(100) NOT NULL,
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password_hash TEXT NOT NULL,
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created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE projects (
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project_id SERIAL PRIMARY KEY,
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project_name VARCHAR(100) NOT NULL,
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description TEXT,
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start_date DATE,
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end_date DATE,
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owner_id INTEGER REFERENCES users(user_id)
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);
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CREATE TABLE tasks (
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task_id SERIAL PRIMARY KEY,
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task_name VARCHAR(100) NOT NULL,
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description TEXT,
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due_date DATE,
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status VARCHAR(50),
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project_id INTEGER REFERENCES projects(project_id)
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);
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CREATE TABLE taskassignments (
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assignment_id SERIAL PRIMARY KEY,
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task_id INTEGER REFERENCES tasks(task_id),
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user_id INTEGER REFERENCES users(user_id),
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assigned_date DATE NOT NULL DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE comments (
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comment_id SERIAL PRIMARY KEY,
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content TEXT NOT NULL,
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created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
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task_id INTEGER REFERENCES tasks(task_id),
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user_id INTEGER REFERENCES users(user_id)
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);
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```
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### Example SQL Outputs
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**Question**: **Show me the day with the most users joining**
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```sql
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SELECT created_at::DATE AS day, COUNT(*) AS user_count
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FROM users
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GROUP BY day
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ORDER BY user_count DESC
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LIMIT 1;
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```
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**Question**: **Show me the project that has a task with the most comments**
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```sql
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SELECT p.project_name, t.task_name, COUNT(c.comment_id) AS comment_count
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FROM projects p
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JOIN tasks t ON p.project_id = t.project_id
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JOIN comments c ON t.task_id = c.task_id
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GROUP BY p.project_name, t.task_name
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ORDER BY comment_count DESC
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LIMIT 1;
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```
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**Question**: **What is the ratio of users with gmail addresses vs without?**
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```sql
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SELECT
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SUM(CASE WHEN email ILIKE '%@gmail.com%' THEN 1 ELSE 0 END)::FLOAT / NULLIF(SUM(CASE WHEN email NOT ILIKE '%@gmail.com%' THEN 1 ELSE 0 END), 0) AS gmail_ratio
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FROM
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users;
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```
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config.json
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"_name_or_path": "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
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"architectures": [
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"LlamaForCausalLM"
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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": 100000,
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"eos_token_id": 100001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 30,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.38.0.dev0",
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"use_cache": true,
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"vocab_size": 102400
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}
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"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin▁of▁sentence|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end▁of▁sentence|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|end▁of▁sentence|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
200040
tokenizer.json
Normal file
200040
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
145
tokenizer_config.json
Normal file
145
tokenizer_config.json
Normal file
@@ -0,0 +1,145 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"100000": {
|
||||
"content": "<|begin▁of▁sentence|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100001": {
|
||||
"content": "<|end▁of▁sentence|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100002": {
|
||||
"content": "ø",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100003": {
|
||||
"content": "ö",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100004": {
|
||||
"content": "ú",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100005": {
|
||||
"content": "ÿ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100006": {
|
||||
"content": "õ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100007": {
|
||||
"content": "÷",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100008": {
|
||||
"content": "û",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100009": {
|
||||
"content": "ý",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100010": {
|
||||
"content": "À",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100011": {
|
||||
"content": "ù",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100012": {
|
||||
"content": "Á",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100013": {
|
||||
"content": "þ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100014": {
|
||||
"content": "ü",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100015": {
|
||||
"content": "<|EOT|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|begin▁of▁sentence|>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|end▁of▁sentence|>",
|
||||
"legacy": true,
|
||||
"model_max_length": 4096,
|
||||
"pad_token": "<|end▁of▁sentence|>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user