README.md

---
license: apache-2.0
datasets:
- bigcode/the-stack
- bigcode/the-stack-v2
- bigcode/starcoderdata
- bigcode/commitpack
library_name: transformers
tags:
- code
base_model:
- JetBrains/Mellum-4b-base
model-index:
- name: Mellum-4b-sft-python
  results:
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python)
    metrics:
    - name: EM
      type: exact_match
      value: 0.2837
      verified: false
    - name: EM ≤ 8k
      type: exact_match
      value: 0.2987
      verified: false
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python, 2k)
    metrics:
    - name: EM
      type: exact_match
      value: 0.2924
      verified: false
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python, 4k)
    metrics:
    - name: EM
      type: exact_match
      value: 0.3060
      verified: false
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python, 8k)
    metrics:
    - name: EM
      type: exact_match
      value: 0.2977
      verified: false
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python, 12k)
    metrics:
    - name: EM
      type: exact_match
      value: 0.2680
      verified: false
  - task:
      type: text-generation
    dataset:
      type: tianyang/repobench_python_v1.1
      name: RepoBench 1.1 (Python, 16k)
    metrics:
    - name: EM
      type: exact_match
      value: 0.2543
      verified: false
  - task:
      type: text-generation
    dataset:
      type: gonglinyuan/safim
      name: SAFIM
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.4212
      verified: false
  - task:
      type: text-generation
    dataset:
      type: gonglinyuan/safim
      name: SAFIM (Algorithmic)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.3316
      verified: false
  - task:
      type: text-generation
    dataset:
      type: gonglinyuan/safim
      name: SAFIM (Control)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.3611
      verified: false
  - task:
      type: text-generation
    dataset:
      type: gonglinyuan/safim
      name: SAFIM (API)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.5710
      verified: false
  - task:
      type: text-generation
    dataset:
      type: loubnabnl/humaneval_infilling
      name: HumanEval Infilling (Single-Line)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.8045
      verified: false
  - task:
      type: text-generation
    dataset:
      type: loubnabnl/humaneval_infilling
      name: HumanEval Infilling (Multi-Line)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.4819
      verified: false
  - task:
      type: text-generation
    dataset:
      type: loubnabnl/humaneval_infilling
      name: HumanEval Infilling (Random Span)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.3768
      verified: false
---

# Model Description
Mellum-4b-sft-python is a fine-tuned version of JetBrains' first open-source large language model (LLM) optimized for code-related tasks.

Pre-trained on over 4 trillion tokens with a context window of 8192 tokens across multiple programming languages, and then fine-tuned, Mellum-4b-sft-python is tailored specifically for code completion in Python. 
The model follows a LLaMA-style architecture with 4 billion parameters, making it efficient for both cloud inference (e.g., via vLLM) and local deployment (e.g., using llama.cpp or Ollama).

Mellum was trained using Automatic Mixed Precision (AMP) with bf16 precision. 
The uploaded version on Hugging Face retains the bf16 format for public use.

Designed for integration into professional developer tooling (e.g., intelligent code suggestions in IDEs), AI-powered coding assistants, and research on code understanding and generation, Mellum is also well-suited for educational applications and fine-tuning experiments.

# Limitations
- Biases: May reflect biases present in public codebases. For example it will likely produce code which is similar in style to the open-source repositories.
- Security: Code suggestions should not be assumed to be secure or free of vulnerabilities.

# Sample Usage
Here are examples of how to run and sample from the model.

## Generic generaion
```python
import json
from transformers import AutoTokenizer, AutoModelForCausalLM

example = """
import sys
import os
import time

sys.path.append(os.getcwd())

from cluster.prepare_data import get_headers_pairs_list, write_dist_matrix
from cluster.token_edit_distance import get_distance_matrix

if len(sys.argv) < 3:
    print(
        "Too few arguments. You should provide: \n1. dataset_filename" +
        "\n2. output_data_filename"
    )
    sys.exit()

start = time.perf_counter()
dataset_filename_ = sys.argv[1]
output_data_filename_ = sys.argv[2]

headers_pairs = get_headers_pairs_list(dataset_filename_, verbose=True)

dist_matrix, max_dist = get_distance_matrix(
    list(map(lambda x: x[1], headers_pairs)),
    verbose=True
)

write_dist_matrix(dist_matrix, max_dist, output_data_filename_, verbose=True)

end = time.perf_counter()
"""

tokenizer = AutoTokenizer.from_pretrained('JetBrains/Mellum-4b-sft-python')
model = AutoModelForCausalLM.from_pretrained('JetBrains/Mellum-4b-sft-python')
encoded_input = tokenizer(example, return_tensors='pt', return_token_type_ids=False)
input_len = len(encoded_input["input_ids"][0])
out = model.generate(
    **encoded_input,
    max_new_tokens=100,
)
print("### Context")
print(tokenizer.decode(out[0][:input_len]))
print("### Prediction")
print(tokenizer.decode(out[0][input_len:]))
```

## Fill in the middle with additional files as context generation
```python
example = """<filename>utils.py
def multiply(x, y):
    return x * y
<filename>config.py
DEBUG = True
MAX_VALUE = 100
<filename>example.py
<fim_suffix> 

# Test the function
result = calculate_sum(5, 10)
print(result)<fim_prefix>def calculate_sum(a, b):
<fim_middle>"""

encoded_input = tokenizer(example, return_tensors='pt', return_token_type_ids=False)
out = model.generate(
    **encoded_input,
    max_new_tokens=100,
)
```

# Citation
If you use this model, please cite:

```bibtex
@misc{Mellum-4b-base,
  title     = {Mellum-4b-base},
  author    = {Pavlichenko, Nikita and Nazarov, Iurii and Dolgov, Ivan and Garanina, Ekaterina and Lasocki, Karol and Reshetnikova, Julia and Boitsov, Sergei and Bondyrev, Ivan and Karaeva, Dariia and Sheptyakov, Maksim and Ustalov, Dmitry and Abramov, Nikita and Kolomyttseva, Olga and Lysaniuk, Kseniia and Zavidnyi, Ilia and Semenkin, Anton and Tankov, Vladislav and Sazanovich, Uladzislau},
  year      = {2025},
}
```

# Contact
For questions, collaborations and requests reach us out via [email protected]