---
title: glaive-coder-7b
canonical_url: "https://www.modelscope.cn/models/Xorbits/glaive-coder-7b"
md_url: "https://www.modelscope.cn/models/Xorbits/glaive-coder-7b.md"
repository: Xorbits/glaive-coder-7b
last_updated: 2023-10-29
license: llama2
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
library_name:
  - transformer
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 68
stars: 1
tags:
  - code
---

# glaive-coder-7b

> glaive-coder-7b - Xorbits 在 ModelScope 开源的模型。Glaive-coder-7b is a 7B parameter code model trained on a dataset of 140k programming related problems and solutions generated from Glaive’s synthetic data generation platform.

Xorbits/glaive-coder-7b 是 ModelScope 魔搭社区上的text-generation模型，采用 llama2 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: Xorbits/glaive-coder-7b
- **License**: llama2
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: code
- **Downloads**: 68
- **Stars**: 1
- **Last updated**: 2023-10-29

Source: https://www.modelscope.cn/models/Xorbits/glaive-coder-7b

---

# Glaive-coder-7b

Glaive-coder-7b is a 7B parameter code model trained on a dataset of ~140k programming related problems and solutions generated from Glaive’s synthetic data generation platform.

The model is fine-tuned on the CodeLlama-7b model.

## Usage:

The model is trained to act as a code assistant, and can do both single instruction following and multi-turn conversations.
It follows the same prompt format as CodeLlama-7b-Instruct-
```
<s>[INST]
<<SYS>>
{{ system_prompt }}
<</SYS>>

{{ user_msg }} [/INST] {{ model_answer }} </s>
<s>[INST] {{ user_msg }} [/INST]
```

You can run the model in the following way-

```python
from transformers import AutoModelForCausalLM , AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b")
model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b").half().cuda()

def fmt_prompt(prompt):
    return f"<s> [INST] {prompt} [/INST]"

inputs = tokenizer(fmt_prompt(prompt),return_tensors="pt").to(model.device)

outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100)

print(tokenizer.decode(outputs[0],skip_special_tokens=True,clean_up_tokenization_spaces=False))
```

## Benchmarks:

The model achieves a 63.1% pass@1 on HumanEval and a 45.2% pass@1 on MBPP, however it is evident that these benchmarks are not representative of real-world usage of code models so we are launching the [Code Models Arena](https://arena.glaive.ai/) to let users vote on model outputs so we can have a better understanding of user preference on code models and come up with new and better benchmarks. We plan to release the Arena results as soon as we have a sufficient amount of data.

Join the Glaive [discord](https://discord.gg/fjQ4uf3yWD) for improvement suggestions, bug-reports and collaborating on more open-source projects.