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Browse files- .gitattributes +26 -0
- README.md +55 -0
- imatrix.dat +3 -0
- stable-code-instruct-3b-IQ1_M.gguf +3 -0
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- stable-code-instruct-3b-Q8_0.gguf +3 -0
.gitattributes
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README.md
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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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- stabilityai
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- gguf
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- imatrix
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- stable-code-instruct-3b
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---
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Quantizations of https://huggingface.co/stabilityai/stable-code-instruct-3b
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# From original readme
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## Usage
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Here's how you can run the model use the model:
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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-instruct-3b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True)
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model.eval()
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model = model.cuda()
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messages = [
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{
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"role": "system",
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"content": "You are a helpful and polite assistant",
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},
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{
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"role": "user",
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"content": "Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes."
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},
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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inputs = tokenizer([prompt], 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=1024,
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temperature=0.5,
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top_p=0.95,
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top_k=100,
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do_sample=True,
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use_cache=True
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)
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output = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0]
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```
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```
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