Upload README.md

README.md

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+---
+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>