duyntnet/qwen2.5-coder-3b-instruct-imatrix-gguf overview

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    "readme_markdown": "---\nlicense: other\nlanguage:\n- en\npipeline_tag: text-generation\ninference: false\ntags:\n- transformers\n- gguf\n- imatrix\n- Qwen2.5-Coder-3B-Instruct\n---\nQuantizations of https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct\n\n\n### Inference Clients/UIs\n* [llama.cpp](https://github.com/ggerganov/llama.cpp)\n* [KoboldCPP](https://github.com/LostRuins/koboldcpp)\n* [ollama](https://github.com/ollama/ollama)\n* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n* [GPT4All](https://github.com/nomic-ai/gpt4all)\n* [jan](https://github.com/janhq/jan)\n---\n\n# From original readme\n\n## Introduction\n\nQwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:\n\n- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.\n- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.\n\n**This repo contains the instruction-tuned 3B Qwen2.5-Coder model**, which has the following features:\n- Type: Causal Language Models\n- Training Stage: Pretraining & Post-training\n- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings\n- Number of Parameters: 3.09B\n- Number of Paramaters (Non-Embedding): 2.77B\n- Number of Layers: 36\n- Number of Attention Heads (GQA): 16 for Q and 2 for KV\n- Context Length: Full 32,768 tokens\n  \nFor more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).\n\n## Requirements\n\nThe code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.\n\nWith `transformers<4.37.0`, you will encounter the following error:\n```\nKeyError: 'qwen2'\n```\n\n## Quickstart\n\nHere provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_name = \"Qwen/Qwen2.5-Coder-3B-Instruct\"\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_name,\n    torch_dtype=\"auto\",\n    device_map=\"auto\"\n)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nprompt = \"write a quick sort algorithm.\"\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\"},\n    {\"role\": \"user\", \"content\": prompt}\n]\ntext = tokenizer.apply_chat_template(\n    messages,\n    tokenize=False,\n    add_generation_prompt=True\n)\nmodel_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n\ngenerated_ids = model.generate(\n    **model_inputs,\n    max_new_tokens=512\n)\ngenerated_ids = [\n    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n]\n\nresponse = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n```",
    "related_quantizations": []
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  "tags": [
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    "gguf",
    "imatrix",
    "Qwen2.5-Coder-3B-Instruct",
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