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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: 23.0
---
# **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](https://github.com/nuprl/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
```python
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](https://huggingface.co/stabilityai/stable-code-instruct-3b/blob/main/LICENSE.md)**.
For commercial use, refer to [Stability AI licensing terms](https://stability.ai/license).
---
## 📌 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**.
|