Update README.md

README.md

@@ -6,4 +6,46 @@ language:
 - en
 tags:
 - code
----
+---
+
+# 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.