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+Yi-Coder-9B-Chat.imatrix.dat filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ1_M.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ1_S.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ2_S.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ2_XS.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ2_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ3_XS.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-Coder-9B-Chat.IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,305 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- code
+language:
+- code
+base_model: 01-ai/Yi-Coder-9B-Chat
+model_creator: 01.AI
+model_name: Yi-Coder-9B-Chat
+model_type: llama
+datasets:
+- m-a-p/CodeFeedback-Filtered-Instruction
+quantized_by: CISC
+---
+
+# Yi-Coder-9B-Chat - SOTA GGUF
+- Model creator: [01.AI](https://huggingface.co/01-ai)
+- Original model: [Yi-Coder-9B-Chat](https://huggingface.co/01-ai/Yi-Coder-9B-Chat)
+
+<!-- description start -->
+## Description
+
+This repo contains State Of The Art quantized GGUF format model files for [Yi-Coder-9B-Chat](https://huggingface.co/01-ai/Yi-Coder-9B-Chat).
+
+Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of answers from the [CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset.
+
+Corrected EOS (<|im_end|>) and added EOT (<|endoftext|>) token to prevent infinite responses (am I the only one actually dog-fooding my own quants?).
+
+Fill-in-Middle token metadata has been added, see [example](#simple-llama-cpp-python-example-fill-in-middle-code). NOTE: Yi's FIM requires support for [SPM infill mode](https://github.com/abetlen/llama-cpp-python/pull/1492)! However it seems it has not been extensively trained for this (perhaps not at all), so don't expect particularly great results...
+
+<!-- description end -->
+
+
+<!-- prompt-template start -->
+## Prompt template: ChatML
+
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+
+<!-- prompt-template end -->
+
+
+<!-- compatibility_gguf start -->
+## Compatibility
+
+These quantised GGUFv3 files are compatible with llama.cpp from February 27th 2024 onwards, as of commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307)
+
+They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
+
+## Explanation of quantisation methods
+
+<details>
+  <summary>Click to see details</summary>
+
+The new methods available are:
+
+* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
+* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
+* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
+* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
+* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
+* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
+* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
+* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
+* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
+* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
+* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
+* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw
+
+Refer to the Provided Files table below to see what files use which methods, and how.
+</details>
+<!-- compatibility_gguf end -->
+
+<!-- README_GGUF.md-provided-files start -->
+## Provided files
+
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [Yi-Coder-9B-Chat.IQ1_S.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ1_S.gguf) | IQ1_S | 1 | 1.9 GB| 2.2 GB | smallest, significant quality loss |
+| [Yi-Coder-9B-Chat.IQ1_M.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ1_M.gguf) | IQ1_M | 1 | 2.0 GB| 2.3 GB | very small, significant quality loss |
+| [Yi-Coder-9B-Chat.IQ2_XXS.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_XXS.gguf) | IQ2_XXS | 2 | 2.3 GB| 2.6 GB | very small, high quality loss |
+| [Yi-Coder-9B-Chat.IQ2_XS.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_XS.gguf) | IQ2_XS | 2 | 2.5 GB| 2.8 GB | very small, high quality loss |
+| [Yi-Coder-9B-Chat.IQ2_S.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_S.gguf) | IQ2_S | 2 | 2.7 GB| 2.9 GB | small, substantial quality loss |
+| [Yi-Coder-9B-Chat.IQ2_M.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ2_M.gguf) | IQ2_M | 2 | 2.9 GB| 3.1 GB | small, greater quality loss |
+| [Yi-Coder-9B-Chat.IQ3_XXS.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_XXS.gguf) | IQ3_XXS | 3 | 3.2 GB| 3.5 GB | very small, high quality loss |
+| [Yi-Coder-9B-Chat.IQ3_XS.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_XS.gguf) | IQ3_XS | 3 | 3.5 GB| 3.8 GB | small, substantial quality loss |
+| [Yi-Coder-9B-Chat.IQ3_S.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_S.gguf) | IQ3_S | 3 | 3.6 GB| 3.9 GB | small, greater quality loss |
+| [Yi-Coder-9B-Chat.IQ3_M.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ3_M.gguf) | IQ3_M | 3 | 3.8 GB| 4.1 GB | medium, balanced quality - recommended |
+| [Yi-Coder-9B-Chat.IQ4_XS.gguf](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.IQ4_XS.gguf) | IQ4_XS | 4 | 4.5 GB| 4.7 GB | small, substantial quality loss |
+
+Generated importance matrix file: [Yi-Coder-9B-Chat.imatrix.dat](https://huggingface.co/CISCai/Yi-Coder-9B-Chat-SOTA-GGUF/blob/main/Yi-Coder-9B-Chat.imatrix.dat)
+
+**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
+
+<!-- README_GGUF.md-provided-files end -->
+
+<!-- README_GGUF.md-how-to-run start -->
+## Example `llama.cpp` command
+
+Make sure you are using `llama.cpp` from commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307) or later.
+
+```shell
+./llama-cli -ngl 49 -m Yi-Coder-9B-Chat.IQ4_XS.gguf --color -c 131072 --temp 0 --repeat-penalty 1.1 -p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+```
+
+Change `-ngl 49` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
+
+Change `-c 131072` to the desired sequence length.
+
+If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size).
+There is a similar option for V-cache (`-ctv`), only available if you enable Flash Attention (`-fa`) as well.
+
+For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+## How to run from Python code
+
+You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module.
+
+### How to load this model in Python code, using llama-cpp-python
+
+For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/).
+
+#### First install the package
+
+Run one of the following commands, according to your system:
+
+```shell
+# Prebuilt wheel with basic CPU support
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
+# Prebuilt wheel with NVidia CUDA acceleration
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
+# Prebuilt wheel with Metal GPU acceleration
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
+# Build base version with no GPU acceleration
+pip install llama-cpp-python
+# With NVidia CUDA acceleration
+CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python
+# Or with OpenBLAS acceleration
+CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
+# Or with AMD ROCm GPU acceleration (Linux only)
+CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python
+# Or with Metal GPU acceleration for macOS systems only
+CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python
+# Or with Vulkan acceleration
+CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python
+# Or with SYCL acceleration
+CMAKE_ARGS="-DGGML_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python
+
+# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
+$env:CMAKE_ARGS = "-DGGML_CUDA=on"
+pip install llama-cpp-python
+```
+
+#### Simple llama-cpp-python example code
+
+```python
+from llama_cpp import Llama
+
+# Chat Completion API
+
+llm = Llama(model_path="./Yi-Coder-9B-Chat.IQ4_XS.gguf", n_gpu_layers=49, n_ctx=131072)
+print(llm.create_chat_completion(
+    repeat_penalty = 1.1,
+    messages = [
+        {
+            "role": "user",
+            "content": "Pick a LeetCode challenge and solve it in Python."
+        }
+    ]
+))
+```
+
+#### Simple llama-cpp-python example fill-in-middle code
+
+```python
+from llama_cpp import Llama
+
+# Completion API
+
+prompt = "def add("
+suffix = "\n    return sum\n\n"
+
+llm = Llama(model_path="./Yi-Coder-9B-Chat.IQ4_XS.gguf", n_gpu_layers=49, n_ctx=131072, spm_infill=True)
+output = llm.create_completion(
+    temperature = 0.0,
+    repeat_penalty = 1.0,
+    prompt = prompt,
+    suffix = suffix
+)
+
+# Models sometimes repeat suffix in response, attempt to filter that
+response = output["choices"][0]["text"]
+response_stripped = response.rstrip()
+unwanted_response_suffix = suffix.rstrip()
+unwanted_response_length = len(unwanted_response_suffix)
+
+filtered = False
+if unwanted_response_suffix and response_stripped[-unwanted_response_length:] == unwanted_response_suffix:
+    response = response_stripped[:-unwanted_response_length]
+    filtered = True
+
+print(f"Fill-in-Middle completion{' (filtered)' if filtered else ''}:\n\n{prompt}\033[32m{response}\033[{'33' if filtered else '0'}m{suffix}\033[0m")
+```
+
+<!-- README_GGUF.md-how-to-run end -->
+
+<!-- original-model-card start -->
+<div align="center">
+
+<picture> 
+  <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
+</picture>
+
+</div>
+
+<p align="center">
+  <a href="https://github.com/01-ai">🐙 GitHub</a> •
+  <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
+  <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
+  <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> 
+  <br/>
+  <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
+  <a href="https://01-ai.github.io/">💪 Tech Blog</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
+</p>
+
+# Intro
+
+Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. 
+
+Key features:
+- Excelling in long-context understanding with a maximum context length of 128K tokens.
+- Supporting 52 major programming languages:
+```bash
+  'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
+  ```
+
+For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
+
+<p align="left"> 
+  <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/> 
+</p>
+
+# Models
+
+| Name               | Type |  Length | Download                                                                                                                                          |
+|--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
+| Yi-Coder-9B-Chat   | Chat |      128K      | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) |
+| Yi-Coder-1.5B-Chat | Chat |      128K      | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) |
+| Yi-Coder-9B        | Base |      128K      | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) |
+| Yi-Coder-1.5B      | Base |      128K      | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) |
+|                    | 
+
+# Benchmarks
+
+As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
+
+<p align="left"> 
+  <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/> 
+</p>
+
+# Quick Start
+
+You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda" # the device to load the model onto
+model_path = "01-ai/Yi-Coder-9B-Chat"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
+
+prompt = "Write a quick sort algorithm."
+messages = [
+    {"role": "system", "content": "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(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=1024,
+    eos_token_id=tokenizer.eos_token_id  
+)
+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]
+print(response)
+```
+
+For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).

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