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Chinese Llama 2 7B

Chinese Llama 2 7B is an open-source, 7-billion-parameter Chinese-language LLaMA 2 chat model with model weights and inference examples in its repository. It follows the Llama 2 Chat prompt format and is accompanied by Chinese-English supervised fine-tuning data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Chinese Llama 2 7B — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a Chinese-language LLaMA 2 model to run or adapt, and can choose an execution path that fits your hardware: hosted demo, CUDA inference, or GGML conversion for a CPU-oriented workflow.

Who it is for

  • Developers prototyping Chinese-language text generation or assistant behavior with an open model.
  • Researchers and practitioners who want to run inference using Transformers or explore the documented GGML conversion route.
  • People who want to try the model through the linked Hugging Face demo or Colab notebooks.

Documented uses

  • Generate Chinese text from prompts using the published model.
  • Prototype a Chinese-language assistant backed by the model.
  • Try the model through the linked hosted demo or notebook options.
  • Prepare a GGML Q4_0 model file for the documented CPU-oriented llama.cpp workflow.
Before you choose

Requirements & limits

The repository links a Hugging Face demo, FP16 and INT4 Colab notebooks, and model downloads on Hugging Face, ModelScope and WiseModel. The README says both Colab notebooks require high RAM and cannot run on the free Colab tier. The repository lists the model under Apache-2.0.

  • The repository's Python inference example requires CUDA and loads the model in half precision.
  • The CPU-oriented route requires conversion and quantization rather than using the basic Transformers example unchanged.
  • FP16 and INT4 Colab notebook routes are both documented, but the README says they require high RAM.
  • The project specifies Llama 2 Chat prompt formatting, which matters when adapting code built for other chat formats.
  • The repository includes Chinese-English fine-tuning data as a separate resource alongside the model.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the model with Transformers on a CUDA GPU

Load the model and generate a response to a formatted prompt using the repository's Python example.

Before you start

  • Python environment with the Transformers library and PyTorch.
  • Access to the model at LinkSoul/Chinese-Llama-2-7b.
  • CUDA-capable GPU, as the example moves the model and input tensors to CUDA.

Steps

  1. Load the tokenizer and model from LinkSoul/Chinese-Llama-2-7b using the repository's Transformers example.

  2. Format an instruction using the Llama 2 Chat prompt structure shown in the README.

  3. Tokenize the prompt, move the input IDs to CUDA, and call model.generate with the example's streamer.

Expected outcome

Generated text is streamed from the loaded Chinese LLaMA 2 7B model.

Guide limitations

  • The example uses half-precision model weights and CUDA; it does not document CPU execution for this route.
  • Hardware capacity requirements beyond the documented CUDA use are not specified.
Documentation 1 ↗
Source-based practical guide

Convert and quantize a model for the documented GGML route

Convert the model files to GGML and produce a Q4_0 quantized file for the llama.cpp-oriented CPU workflow.

Before you start

  • The model files in the documented /app/LinkSoul/Chinese-Llama-2-7b/ location, or equivalent paths adjusted for the conversion commands.
  • The conversion script or converter image and the quantize utility described by the repository.

Steps

  1. Use ggml/convert_to_ggml.py, referring to its supported CLI parameters, or use the documented converter image.

  2. Run the documented conversion command to create a GGML binary file.

  3. Run the quantize command with q4_0 to create a quantized binary.

Expected outcome

A GGML Q4_0 model file is created for the repository's described CPU-oriented llama.cpp workflow.

Guide limitations

  • The README refers to conversion script parameters and a separate tutorial; it does not give a complete llama.cpp execution procedure in the provided instructions.
  • The recipe does not establish performance or hardware compatibility.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Chinese Llama 2 7BGenerate Chinese text from prompts using the published model.The repository links a Hugging Face demo, FP16 and INT4 Colab notebooks, and model downloads on Hugging Face, ModelScope and WiseModel. The README says both Colab notebooks require high RAM and cannot run on the free Colab tier. The repository lists the model under Apache-2.0.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
LinkSoul-AI/Chinese-Llama-2-7b · Official homepage title · retained in its source language
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