Quant for 6.5

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -13,72 +13,123 @@ tags:
 - chat
 - qwen
 - qwen-coder
-quantized_by: bartowski
 ---
 
-## Exllama v2 Quantizations of Qwen2.5-Coder-32B-Instruct
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.2.3">turboderp's ExLlamaV2 v0.2.3</a> for quantization.
+# Qwen2.5-Coder-32B-Instruct
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+## Introduction
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+Qwen2.5-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:
 
-Conversion was done using the default calibration dataset.
+- 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.
+- 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.
+- **Long-context Support** up to 128K tokens.
 
-Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
+**This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- Number of Parameters: 32.5B
+- Number of Paramaters (Non-Embedding): 31.0B
+- Number of Layers: 64
+- Number of Attention Heads (GQA): 40 for Q and 8 for KV
+- Context Length: Full 131,072 tokens
+  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
+  
+For 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).
 
-Original model: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct
+## Requirements
 
+The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
 
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/8_0">8.0 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/6_5">6.5 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/5_0">5.0 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/4_25">4.25 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/3_5">3.5 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/3_0">3.0 bits per weight</a>
-
-<a href="https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2/tree/2_2">2.2 bits per weight</a>
-
-
-## Download instructions
-
-With git:
-
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-exl2
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
 ```
 
-With huggingface hub (credit to TheBloke for instructions):
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "write a quick sort algorithm."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
-```shell
-pip3 install huggingface-hub
+### Processing Long Texts
+
+The current `config.json` is set for context length up to 32,768 tokens.
+To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
+
+For supported frameworks, you could add the following to `config.json` to enable YaRN:
+```json
+{
+  ...,
+  "rope_scaling": {
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768,
+    "type": "yarn"
+  }
+}
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Qwen2.5-Coder-32B-Instruct-exl2`:
+For deployment, we recommend using vLLM. 
+Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
+Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. 
+We advise adding the `rope_scaling` configuration only when processing long contexts is required.
 
-```shell
-mkdir Qwen2.5-Coder-32B-Instruct-exl2
-huggingface-cli download bartowski/Qwen2.5-Coder-32B-Instruct-exl2 --local-dir Qwen2.5-Coder-32B-Instruct-exl2
-```
+## Evaluation & Performance
 
-To download from a different branch, add the `--revision` parameter:
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
 
-Linux:
+For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
 
-```shell
-mkdir Qwen2.5-Coder-32B-Instruct-exl2-6_5
-huggingface-cli download bartowski/Qwen2.5-Coder-32B-Instruct-exl2 --revision 6_5 --local-dir Qwen2.5-Coder-32B-Instruct-exl2-6_5
-```
+## Citation
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+If you find our work helpful, feel free to give us a cite.
 
-```shell
-mkdir Qwen2.5-Coder-32B-Instruct-exl2-6.5
-huggingface-cli download bartowski/Qwen2.5-Coder-32B-Instruct-exl2 --revision 6_5 --local-dir Qwen2.5-Coder-32B-Instruct-exl2-6.5
+```
+@article{hui2024qwen2,
+      title={Qwen2. 5-Coder Technical Report},
+      author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
+      journal={arXiv preprint arXiv:2409.12186},
+      year={2024}
+}
+@article{qwen2,
+      title={Qwen2 Technical Report}, 
+      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
+      journal={arXiv preprint arXiv:2407.10671},
+      year={2024}
+}
 ```

config.json

@@ -0,0 +1,38 @@
+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 27648,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "model_type": "qwen2",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 64,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 1000000.0,
+    "sliding_window": 131072,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 152064,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.2.3",
+        "bits": 6.5,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

model.safetensors.index.json

@@ -0,0 +1,778 @@
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