This model was converted to GGUF format from BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

bash

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-Q8_0-GGUF --hf-file yi-coder-9b-chat-instruct-ties-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-Q8_0-GGUF --hf-file yi-coder-9b-chat-instruct-ties-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-Q8_0-GGUF --hf-file yi-coder-9b-chat-instruct-ties-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-Q8_0-GGUF --hf-file yi-coder-9b-chat-instruct-ties-q8_0.gguf -c 2048

base_model:

01-ai/Yi-Coder-9B-Chat
01-ai/Yi-Coder-9B library_name: transformers tags:
mergekit
merge license: apache-2.0

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using 01-ai/Yi-Coder-9B as a base.

Models Merged

The following models were included in the merge:

01-ai/Yi-Coder-9B-Chat

Configuration

The following YAML configuration was used to produce this model:

yaml

models:
  - model: 01-ai/Yi-Coder-9B
    parameters:
      density: 0.5
      weight: 0.5
  - model: 01-ai/Yi-Coder-9B-Chat
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: 01-ai/Yi-Coder-9B
parameters:
  normalize: false
  int8_mask: true
dtype: float16

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📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 Learning Hub

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:

  '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 and Yi-Coder README.

Models

Name	Type	Length	Download
Yi-Coder-9B-Chat	Chat	128K	🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-Coder-1.5B-Chat	Chat	128K	🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-Coder-9B	Base	128K	🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-Coder-1.5B	Base	128K	🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel

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%.

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.