---
license: other
language:
- en
pipeline_tag: text-generation
tags:
- gguf
- imatrix
- stable-code-3b
- stabilityai
---
Quantizations of https://huggingface.co/stabilityai/stable-code-3b

# From original readme

## Usage

Get started generating text with `stable-code-3b` by using the following code snippet:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stable-code-3b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=48,
  temperature=0.2,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```

### Run with Fill in Middle (FIM) ⚡️

<details>
<summary> Click to expand </summary>

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stable-code-3b",
  torch_dtype="auto",
  attn_implementation="flash_attention_2",
)
model.cuda()
inputs = tokenizer("<fim_prefix>def fib(n):<fim_suffix>    else:\n        return fib(n - 2) + fib(n - 1)<fim_middle>", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=48,
  temperature=0.2,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```

</details>

### Run with Flash Attention 2 ⚡️

<details>
<summary> Click to expand </summary>

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stable-code-3b",
  trust_remote_code=True,
  torch_dtype="auto",
+ attn_implementation="flash_attention_2",
)
model.cuda()
inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=48,
  temperature=0.2,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```

</details>