README.md

metadata

license: other
language:
  - en
tags:
  - causal-lm
  - code
metrics:
  - code_eval
library_name: transformers
model-index:
  - name: dgtalbug/stable-code-instruct-3b
    results:
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Python)
        metrics:
          - name: pass@1
            type: pass@1
            value: 32.4
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (C++)
        metrics:
          - name: pass@1
            type: pass@1
            value: 30.9
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 32.1
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 32.1
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (PHP)
        metrics:
          - name: pass@1
            type: pass@1
            value: 24.2
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL-HumanEval (Rust)
        metrics:
          - name: pass@1
            type: pass@1
            value: 23

Stable Code Instruct 3B — Base Model

This repository stores an unchanged copy of stabilityai/stable-code-instruct-3b
for use as a base model in future fine‑tuning projects (including Stephen).

---

📌 About the Model

stable-code-instruct-3b is a 2.7B parameter decoder-only transformer from Stability AI, tuned for multi‑language code generation and conversational coding assistance.
It is suitable as a starting point for specialized code assistants,
including fine‑tuned variants with domain‑specific datasets.

Key Features:

• General purpose code generation across multiple programming languages.
• Instruction‑tuned for better conversational performance.
• Strong performance on MultiPL-E benchmarks.

---

📊 Performance (MultiPL-E Benchmark)

Language	pass@1
Python	32.4%
C++	30.9%
Java	32.1%
JavaScript	32.1%
PHP	24.2%
Rust	23.0%

---

🚀 Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dgtalbug/stable-code-instruct-3b"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, trust_remote_code=True
).cuda().eval()

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to reverse a string."}
]

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=200,
    temperature=0.5,
    top_p=0.95,
    top_k=100,
    do_sample=True,
    use_cache=True
)

output = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0]
print(output)

---

📜 License

This model follows the Stability AI Community License.
For commercial use, refer to Stability AI licensing terms.

---

📌 Note for Fine‑Tuning

This repository is not modified — it is kept as a clean base model for derivative works.
Fine‑tuned versions (e.g., Stephen) will be released in separate repositories.