初始化项目，由ModelHub XC社区提供模型
Model: OpenDevin/CodeQwen1.5-7B-OpenDevin Source: Original Platform
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README.md
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---
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language:
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- en
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pipeline_tag: text-generation
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---
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# CodeQwen1.5-7B-OpenDevin
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## Introduction
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CodeQwen1.5-7B-OpenDevin is a code-specific model targeting on OpenDevin Agent tasks.
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The model is finetuned from CodeQwen1.5-7B, the code-specific large language model based on Qwen1.5 pretrained on large-scale code data.
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CodeQwen1.5-7B is strongly capable of understanding and generating codes, and it supports the context length of 65,536 tokens (for more information about CodeQwen1.5, please refer to the [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5)).
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The finetuned model, CodeQwen1.5-7B-OpenDevin, shares similar features, while it is designed for rapid development, debugging, and iteration.
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## Performance
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We evaluate CodeQwen1.5-7B-OpenDevin on SWE-Bench-Lite by implementing the model on OpenDevin CodeAct 1.3 and follow the OpenDevin evaluation pipeline.
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CodeQwen1.5-7B-OpenDevin successfully solves 4 problems by commmiting pull requests targeting on the issues.
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## Requirements
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The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
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```
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KeyError: 'qwen2'.
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```
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## Quickstart
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To use local models to run OpenDevin, we advise you to deploy CodeQwen1.5-7B-OpenDevin on a GPU device and access it through OpenAI API
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```bash
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python -m vllm.entrypoints.openai.api_server --model OpenDevin/CodeQwen1.5-7B-OpenDevin --dtype auto --api-key token-abc123
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```
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For more details, please refer to the official documentation of [vLLM for OpenAI Compatible server](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html).
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After the deployment, following the guidance of [OpenDevin](https://github.com/OpenDevin/OpenDevin) and run the following command to set up environment variables:
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```bash
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# The directory you want OpenDevin to work with. MUST be an absolute path!
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export WORKSPACE_BASE=$(pwd)/workspace;
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export LLM_API_KEY=token-abc123;
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export LLM_MODEL=OpenDevin/CodeQwen1.5-7B-OpenDevin;
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export LLM_BASE_URL=http://localhost:8000/v1;
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```
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and run the docker command:
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```bash
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docker run \
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-it \
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--pull=always \
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-e SANDBOX_USER_ID=$(id -u) \
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-e LLM_BASE_URL=$LLM_BASE_URL \
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-e LLM_API_KEY=$LLM_API_KEY \
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-e LLM_MODEL=$LLM_MODEL \
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-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
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-v $WORKSPACE_BASE:/opt/workspace_base \
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-v /var/run/docker.sock:/var/run/docker.sock \
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-p 3000:3000 \
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--add-host host.docker.internal:host-gateway \
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ghcr.io/opendevin/opendevin:0.5
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```
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Now you should be able to connect `http://localhost:3000/`. Set up the configuration at the frontend by clicking the button at the bottom right, and input the right model name and api key.
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Then, you can enjoy playing with OpenDevin based on CodeQwen1.5-7B-OpenDevin!
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## Note
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This is just a finetuning experiment, and we admit that the performance of the model is still lagging far behind GPT-4. In the future, we will update our datasets for agent-specific finetuning and provide better and larger models. Stay tuned!
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## Citation
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```
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@misc{codeqwen1.5,
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title = {Code with CodeQwen1.5},
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url = {https://qwenlm.github.io/blog/codeqwen1.5/},
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author = {Qwen Team},
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month = {April},
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year = {2024}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 13440,
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"max_position_embeddings": 65536,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000,
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"rotary_emb_base": 1000000,
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"seq_length": 65536,
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"sliding_window": 65536,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 92416
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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"bos_token_id": 2,
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"do_sample": true,
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"eos_token_id": [
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4,
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2
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],
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"pad_token_id": 92298,
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"repetition_penalty": 1.0,
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"temperature": 1.0,
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"top_p": 0.95,
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"transformers_version": "4.39.3"
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}
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||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:eb655127a084545dbe5228bee7e666157a132eb6b98a01b0d090e004c357b64b
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||||
size 14500697262
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216896
tokenizer.json
Normal file
216896
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
19
tokenizer_config.json
Normal file
19
tokenizer_config.json
Normal file
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
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||||
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"add_prefix_space": true,
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||||
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user