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stable-code-instruct-3b-imatrix-GGUF

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stabilityai
imatrix
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  1. .gitattributes +26 -0
  2. README.md +55 -0
  3. imatrix.dat +3 -0
  4. stable-code-instruct-3b-IQ1_M.gguf +3 -0
  5. stable-code-instruct-3b-IQ1_S.gguf +3 -0
  6. stable-code-instruct-3b-IQ2_M.gguf +3 -0
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  11. stable-code-instruct-3b-IQ3_S.gguf +3 -0
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  13. stable-code-instruct-3b-IQ3_XXS.gguf +3 -0
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  17. stable-code-instruct-3b-Q2_K_S.gguf +3 -0
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  19. stable-code-instruct-3b-Q3_K_M.gguf +3 -0
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  26. stable-code-instruct-3b-Q5_K_S.gguf +3 -0
  27. stable-code-instruct-3b-Q6_K.gguf +3 -0
  28. stable-code-instruct-3b-Q8_0.gguf +3 -0
.gitattributes CHANGED
@@ -33,3 +33,29 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ imatrix.dat filter=lfs diff=lfs merge=lfs -text
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+ stable-code-instruct-3b-IQ1_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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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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+
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+ # From original readme
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+
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+ ## Usage
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+ Here's how you can run the model use the model:
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+
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+ ```python
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+
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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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+
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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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+
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+ prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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+
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+ inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
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+
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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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+
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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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