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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. |
