Mnemosyne-3B

Mnemosyne-3B is a QLoRA fine-tune of Qwen/Qwen2.5-Coder-3B-Instruct for natural language to SQL generation, with a specialisation in laboratory, scientific, food safety, water quality, and environmental microbiology database schemas.

The model is designed for low-latency local or server-side SQL generation. It is released in full bf16 precision and in GGUF format (Q4_K_M and Q8_0) for use with llama.cpp, Ollama, and LM Studio.

---

Intended Use

Mnemosyne-3B is suited for:

• Generating SQL queries from natural language questions against a provided database schema
• Applications in laboratory information management systems (LIMS), food and water testing, and scientific data management
• General-purpose text-to-SQL use cases where low-latency local inference is required
• Developer tooling, data analyst assistants, and schema-aware chatbots

Mnemosyne-3B is not suited for:

• Tasks requiring external knowledge beyond the provided schema
• Applications without a schema context (schema must be provided at inference time)
• Safety-critical automated execution without a human review step

---

Model Details

---

Training

Hyperparameters

The model converged at step 1000 — eval loss plateaued beyond this point, and checkpoint-1000 was selected as the final model.

Prompt Format

Mnemosyne-3B uses the Qwen2.5 ChatML format with a task-specific system prompt:

<|im_start|>system
You are Mnemosyne, an expert SQL assistant specialising in laboratory,
scientific, food safety, water quality, and general-purpose database queries.
Given a database schema and a natural language question, generate a correct,
well-formatted SQL query. Return only the SQL with no explanation.<|im_end|>
<|im_start|>user
### Schema:
{DDL}

### Question:
{natural_language_question}<|im_end|>
<|im_start|>assistant
{sql_query}<|im_end|>

---

Training Data

Mnemosyne-3B was trained on a combination of three datasets, capped at 20,000 examples per source and shuffled before training:

Combined training set: ~40,579 examples (after quality filtering).

Mnemosyne Lab Dataset

The lab domain dataset was purpose-built for this fine-tune. It covers an 8-table LIMS schema (clients, samples, analysts, methods, determinands, results, worksheets, worksheet_samples) spanning food safety, drinking water, surface water, and environmental microbiology testing.

All examples are entirely synthetic and fictional. Company names, client names, staff names, and sample identifiers are invented. Analyte names and method references (ISO 9308-1, ISO 11290-1, ISO 6579-1, EC 2073/2005, EU DWD 2020/2184, etc.) reflect public international standards and are not proprietary. The dataset contains no real personal data, no real employer information, and no confidential laboratory records.

The dataset covers three complexity tiers:

---

Evaluation

All evaluations use execution accuracy (EX) — result set comparison against a live SQLite database — rather than exact match. This is the gold standard metric for text-to-SQL because syntactically different queries can return identical results. Exact match (EM) and valid SQL rate (VLD) are reported as supplementary metrics.

Results

Interpretation

Mnemosyne-3B demonstrates the expected trade-off of targeted domain fine-tuning: a modest regression on general cross-domain SQL (Spider, -7.8% EX) in exchange for a large gain on laboratory domain SQL (+48% EX overall). The base model scores near-zero (2.6% EX) on complex LIMS queries; Mnemosyne-3B reaches 43.6% — a 17× improvement.

The high exact match on the lab suite (EM=89% vs EX=78%) reflects a known limitation of the evaluation setup: gold SQL was authored in PostgreSQL syntax, and some PostgreSQL-specific functions (DATE_TRUNC, INTERVAL, EXTRACT) are not natively supported by SQLite. Queries where Mnemosyne generates an exact match with the gold SQL may fail execution against SQLite even though the SQL is correct. Real-world execution accuracy on a PostgreSQL deployment would be higher than reported.

---

Limitations

• General SQL regression: Mnemosyne-3B trades approximately 8% Spider EX for domain specialisation. For purely general-purpose SQL use cases, the base Qwen2.5-Coder-3B-Instruct may perform better.
• Schema required at inference time: The model has no implicit knowledge of any specific database. A DDL schema must be provided in every prompt.
• Schema length: Very long schemas (many tables, many columns) may be truncated at the 2048-token context limit. Prioritise relevant tables where possible.
• Complex SQL ceiling: At 3B parameters, performance on multi-CTE, deeply nested, or multi-schema queries is limited. Consider larger models for enterprise-grade analytical SQL.
• Dialect sensitivity: The model was primarily trained on ANSI/PostgreSQL-style SQL. Highly dialect-specific syntax (T-SQL, PL/pgSQL procedural blocks) is not a primary use case.
• No execution or error correction: The model generates SQL in a single forward pass. It does not self-correct on execution errors. Downstream agents should implement error-feedback loops if needed.

---

How to Use

Transformers (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "EphAsad/Mnemosyne-3B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

SYSTEM_PROMPT = (
    "You are Mnemosyne, an expert SQL assistant specialising in laboratory, "
    "scientific, food safety, water quality, and general-purpose database queries. "
    "Given a database schema and a natural language question, generate a correct, "
    "well-formatted SQL query. Return only the SQL with no explanation."
)

def generate_sql(schema: str, question: str) -> str:
    prompt = (
        f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
        f"<|im_start|>user\n"
        f"### Schema:\n{schema.strip()}\n\n"
        f"### Question:\n{question.strip()}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.1,
        do_sample=True,
        eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>"),
    )
    decoded = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True,
    )
    return decoded.split("<|im_end|>")[0].strip()

# General SQL example
schema = """
CREATE TABLE products (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    price FLOAT,
    category VARCHAR(50),
    stock INT
);
"""
question = "Show the top 5 most expensive products in the Electronics category that are still in stock."
print(generate_sql(schema, question))
# SELECT name, price FROM products
# WHERE category = 'Electronics' AND stock > 0
# ORDER BY price DESC LIMIT 5;

# Laboratory domain example
lab_schema = """
CREATE TABLE results (
    result_id SERIAL PRIMARY KEY,
    sample_id VARCHAR(20),
    determinand_id INT,
    numeric_value FLOAT,
    pass_fail CHAR(1),
    test_date DATE
);
CREATE TABLE determinands (
    determinand_id SERIAL PRIMARY KEY,
    determinand_name VARCHAR(100),
    unit VARCHAR(20)
);
CREATE TABLE samples (
    sample_id VARCHAR(20) PRIMARY KEY,
    matrix VARCHAR(50),
    collection_date DATE,
    client_id INT
);
CREATE TABLE clients (
    client_id SERIAL PRIMARY KEY,
    client_name VARCHAR(100)
);
"""
lab_question = "Show all failed E. coli results from drinking water samples in the last 30 days, ordered by numeric value descending."
print(generate_sql(lab_schema, lab_question))

Unsloth (fast inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="EphAsad/Mnemosyne-3B",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

GGUF — Ollama

ollama run EphAsad/Mnemosyne-3B-Q4_K_M

GGUF — llama.cpp

./llama-cli \
  -m Mnemosyne-3B-Q4_K_M.gguf \
  --temp 0.1 \
  -n 256 \
  -p "<|im_start|>system\nYou are Mnemosyne...

---

Available Files

---

Ethical Considerations

Training data privacy: The custom lab domain dataset used in training contains no real personal data, no real employer information, and no confidential laboratory records. All company names, client names, staff names, and identifiers are entirely fictional. Analyte names and method references reflect public international standards (ISO, EN, EPA, EC regulations).

Intended for decision support, not autonomous operation: SQL generated by this model should be reviewed before execution in production systems. The model may produce syntactically valid but semantically incorrect queries, particularly on complex schemas it has not been trained on. Human review is strongly recommended in regulated environments.

Potential for misuse: As with all SQL generation models, outputs should not be executed with elevated database privileges without appropriate access controls. The model has no awareness of data sensitivity or access permissions.

---

Citation

If you use Mnemosyne-3B in your work, please cite:

@misc{asad2025mnemosyne3b,
  author       = {Zain Asad},
  title        = {Mnemosyne-3B: A Domain-Specialised Text-to-SQL Model for Laboratory and Scientific Databases},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/EphAsad/Mnemosyne-3B}
}

---

Support

If you find this model useful for your research or projects, you can support further development of my datasets and models here:
☕ ko-fi.com/ephraim123

Acknowledgements

• Qwen team at Alibaba Cloud for the Qwen2.5-Coder base model
• Unsloth for the efficient QLoRA training framework
• b-mc2 and Gretel AI for the open SQL training datasets