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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- gguf |
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- imatrix |
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- stable-code-3b |
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- stabilityai |
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--- |
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Quantizations of https://huggingface.co/stabilityai/stable-code-3b |
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# From original readme |
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## Usage |
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Get started generating text with `stable-code-3b` by using the following code snippet: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b") |
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model = AutoModelForCausalLM.from_pretrained( |
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"stabilityai/stable-code-3b", |
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torch_dtype="auto", |
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) |
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model.cuda() |
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inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to(model.device) |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=48, |
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temperature=0.2, |
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do_sample=True, |
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) |
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print(tokenizer.decode(tokens[0], skip_special_tokens=True)) |
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``` |
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### Run with Fill in Middle (FIM) ⚡️ |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b") |
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model = AutoModelForCausalLM.from_pretrained( |
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"stabilityai/stable-code-3b", |
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torch_dtype="auto", |
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attn_implementation="flash_attention_2", |
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) |
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model.cuda() |
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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) |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=48, |
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temperature=0.2, |
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do_sample=True, |
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) |
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print(tokenizer.decode(tokens[0], skip_special_tokens=True)) |
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``` |
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</details> |
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### Run with Flash Attention 2 ⚡️ |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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"stabilityai/stable-code-3b", |
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trust_remote_code=True, |
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torch_dtype="auto", |
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+ attn_implementation="flash_attention_2", |
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) |
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model.cuda() |
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inputs = tokenizer("import torch\nimport torch.nn as nn", return_tensors="pt").to(model.device) |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=48, |
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temperature=0.2, |
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do_sample=True, |
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) |
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print(tokenizer.decode(tokens[0], skip_special_tokens=True)) |
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``` |
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</details> |
