Instructions to use kolosal/qwen2.5-coder-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kolosal/qwen2.5-coder-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kolosal/qwen2.5-coder-3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kolosal/qwen2.5-coder-3b", dtype="auto") - llama-cpp-python
How to use kolosal/qwen2.5-coder-3b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kolosal/qwen2.5-coder-3b", filename="qwen2.5-coder-3b-instruct-fp16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kolosal/qwen2.5-coder-3b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Use Docker
docker model run hf.co/kolosal/qwen2.5-coder-3b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use kolosal/qwen2.5-coder-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kolosal/qwen2.5-coder-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kolosal/qwen2.5-coder-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kolosal/qwen2.5-coder-3b:Q4_K_M
- SGLang
How to use kolosal/qwen2.5-coder-3b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "kolosal/qwen2.5-coder-3b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kolosal/qwen2.5-coder-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "kolosal/qwen2.5-coder-3b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kolosal/qwen2.5-coder-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use kolosal/qwen2.5-coder-3b with Ollama:
ollama run hf.co/kolosal/qwen2.5-coder-3b:Q4_K_M
- Unsloth Studio
How to use kolosal/qwen2.5-coder-3b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kolosal/qwen2.5-coder-3b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kolosal/qwen2.5-coder-3b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kolosal/qwen2.5-coder-3b to start chatting
- Pi
How to use kolosal/qwen2.5-coder-3b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "kolosal/qwen2.5-coder-3b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use kolosal/qwen2.5-coder-3b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf kolosal/qwen2.5-coder-3b:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default kolosal/qwen2.5-coder-3b:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use kolosal/qwen2.5-coder-3b with Docker Model Runner:
docker model run hf.co/kolosal/qwen2.5-coder-3b:Q4_K_M
- Lemonade
How to use kolosal/qwen2.5-coder-3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kolosal/qwen2.5-coder-3b:Q4_K_M
Run and chat with the model
lemonade run user.qwen2.5-coder-3b-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- LICENSE +54 -0
- README.md +92 -0
- qwen2.5-coder-3b-instruct-fp16.gguf +3 -0
- qwen2.5-coder-3b-instruct-q2_k.gguf +3 -0
- qwen2.5-coder-3b-instruct-q3_k_m.gguf +3 -0
- qwen2.5-coder-3b-instruct-q4_0.gguf +3 -0
- qwen2.5-coder-3b-instruct-q4_k_m.gguf +3 -0
- qwen2.5-coder-3b-instruct-q5_0.gguf +3 -0
- qwen2.5-coder-3b-instruct-q5_k_m.gguf +3 -0
- qwen2.5-coder-3b-instruct-q6_k.gguf +3 -0
- qwen2.5-coder-3b-instruct-q8_0.gguf +3 -0
.gitattributes
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LICENSE
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Qwen RESEARCH LICENSE AGREEMENT
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Qwen RESEARCH LICENSE AGREEMENT Release Date: September 19, 2024
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By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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1. Definitions
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a. This Qwen RESEARCH LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
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d. "Third Parties" shall mean individuals or legal entities that are not under common control with us or you.
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e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
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f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Qwen and Documentation (and any portion thereof) made available under this Agreement.
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g. "Source" form shall mean the preferred form for making modifications, including but not limited to model source code, documentation source, and configuration files.
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a. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials FOR NON-COMMERCIAL PURPOSES ONLY.
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c. You shall retain in all copies of the Materials that you distribute the following attribution notices within a "Notice" text file distributed as a part of such copies: "Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved."; and
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a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
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b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
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5. Intellectual Property
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a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
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b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.
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6. Disclaimer of Warranty and Limitation of Liability
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a. We are not obligated to support, update, provide training for, or develop any further version of the Qwen Materials or to grant any license thereto.
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b. THE MATERIALS ARE PROVIDED "AS IS" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. WE MAKE NO WARRANTY AND ASSUME NO RESPONSIBILITY FOR THE SAFETY OR STABILITY OF THE MATERIALS AND ANY OUTPUT THEREFROM.
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c. IN NO EVENT SHALL WE BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.
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d. You will defend, indemnify and hold harmless us from and against any claim by any third party arising out of or related to your use or distribution of the Materials.
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7. Survival and Termination.
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a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
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b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 6 and 8 shall survive the termination of this Agreement.
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8. Governing Law and Jurisdiction.
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a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
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b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
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9. Other Terms and Conditions.
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a. Any arrangements, understandings, or agreements regarding the Material not stated herein are separate from and independent of the terms and conditions of this Agreement. You shall request a separate license from us, if you use the Materials in ways not expressly agreed to in this Agreement.
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b. We shall not be bound by any additional or different terms or conditions communicated by you unless expressly agreed.
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README.md
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---
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license: other
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license_name: qwen-research
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license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct-GGUF/blob/main/LICENSE
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-Coder-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- code
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- codeqwen
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- chat
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- qwen
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- qwen-coder
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---
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# Qwen2.5-Coder-3B-Instruct-GGUF
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## Introduction
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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:
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- 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.
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- 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.
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**This repo contains the instruction-tuned 3B Qwen2.5-Coder model in the GGUF Format**, which has the following features:
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- Type: Causal Language Models
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- Training Stage: Pretraining & Post-training
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- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
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- Number of Parameters: 3.09B
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- Number of Paramaters (Non-Embedding): 2.77B
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- Number of Layers: 36
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- Number of Attention Heads (GQA): 16 for Q and 2 for KV
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- Context Length: Full 32,768 tokens
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- Note: Currently, only vLLM supports YARN for length extrapolating. If you want to process sequences up to 131,072 tokens, please refer to non-GGUF models.
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- Quantization: q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0
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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).
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## Quickstart
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| 44 |
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Check out our [llama.cpp documentation](https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html) for more usage guide.
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| 46 |
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| 47 |
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We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide. We follow the latest version of llama.cpp.
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| 48 |
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In the following demonstration, we assume that you are running commands under the repository `llama.cpp`.
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| 49 |
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| 50 |
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Since cloning the entire repo may be inefficient, you can manually download the GGUF file that you need or use `huggingface-cli`:
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| 51 |
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1. Install
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| 52 |
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```shell
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| 53 |
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pip install -U huggingface_hub
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| 54 |
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```
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| 55 |
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2. Download:
|
| 56 |
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```shell
|
| 57 |
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huggingface-cli download Qwen/Qwen2.5-Coder-3B-Instruct-GGUF qwen2.5-3b-coder-instruct-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False
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| 58 |
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```
|
| 59 |
+
|
| 60 |
+
For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:
|
| 61 |
+
|
| 62 |
+
```shell
|
| 63 |
+
./llama-cli -m <gguf-file-path> \
|
| 64 |
+
-co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \
|
| 65 |
+
-fa -ngl 80 -n 512
|
| 66 |
+
```
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
## Evaluation & Performance
|
| 70 |
+
|
| 71 |
+
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
|
| 72 |
+
|
| 73 |
+
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
|
| 74 |
+
|
| 75 |
+
## Citation
|
| 76 |
+
|
| 77 |
+
If you find our work helpful, feel free to give us a cite.
|
| 78 |
+
|
| 79 |
+
```
|
| 80 |
+
@article{hui2024qwen2,
|
| 81 |
+
title={Qwen2. 5-Coder Technical Report},
|
| 82 |
+
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},
|
| 83 |
+
journal={arXiv preprint arXiv:2409.12186},
|
| 84 |
+
year={2024}
|
| 85 |
+
}
|
| 86 |
+
@article{qwen2,
|
| 87 |
+
title={Qwen2 Technical Report},
|
| 88 |
+
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},
|
| 89 |
+
journal={arXiv preprint arXiv:2407.10671},
|
| 90 |
+
year={2024}
|
| 91 |
+
}
|
| 92 |
+
```
|
qwen2.5-coder-3b-instruct-fp16.gguf
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|
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ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
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version https://git-lfs.github.com/spec/v1
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ADDED
|
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version https://git-lfs.github.com/spec/v1
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|
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qwen2.5-coder-3b-instruct-q8_0.gguf
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|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 3616088512
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