53 lines
1.9 KiB
Markdown
53 lines
1.9 KiB
Markdown
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---
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license: llama2
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datasets:
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- glaiveai/glaive-code-assistant
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language:
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- en
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tags:
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- code
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---
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# Glaive-coder-7b
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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.
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The model is fine-tuned on the CodeLlama-7b model.
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## Usage:
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The model is trained to act as a code assistant, and can do both single instruction following and multi-turn conversations.
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It follows the same prompt format as CodeLlama-7b-Instruct-
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```
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<s>[INST]
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<<SYS>>
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{{ system_prompt }}
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<</SYS>>
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{{ user_msg }} [/INST] {{ model_answer }} </s>
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<s>[INST] {{ user_msg }} [/INST]
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```
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You can run the model in the following way-
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```python
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from transformers import AutoModelForCausalLM , AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b")
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model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b").half().cuda()
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def fmt_prompt(prompt):
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return f"<s> [INST] {prompt} [/INST]"
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inputs = tokenizer(fmt_prompt(prompt),return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100)
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print(tokenizer.decode(outputs[0],skip_special_tokens=True,clean_up_tokenization_spaces=False))
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```
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## Benchmarks:
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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.
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Join the Glaive [discord](https://discord.gg/fjQ4uf3yWD) for improvement suggestions, bug-reports and collaborating on more open-source projects.
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