Update README.md

Quantization updated

README.md

@@ -1,3 +1,94 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- open-r1/codeforces-cots
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+- open-r1/OlympicCoder-7B
+base_model_relation: quantized
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+
+## Exllama v2 Quantizations of OlympicCoder-7B
+
+Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.2.8">turboderp's ExLlamaV2 v0.2.8</a> for quantization.
+
+average:4.5bpw
+lm_head:6.0bpw
+```sh
+python convert.py \
+    -i {path}/OlympicCoder-7B \
+    -o {path}/OlympicCoder-7B/workingdir/ \
+    -cf {path}/OlympicCoder-7B_4.5bpw/ \
+    -b 4.5 \
+    -hb 6
+```
+
+# Model Card for OlympicCoder-7B
+
+OlympicCoder-7B is a code model that achieves strong performance on competitive coding benchmarks such as LiveCodeBench and the 2024 International Olympiad in Informatics. 
+
+* Repository: https://github.com/huggingface/open-r1
+* Blog post: https://huggingface.co/blog/open-r1/update-3
+
+## Model description
+
+- **Model type:** A 7B parameter model fine-tuned on a decontaminated version of the codeforces dataset.
+- **Language(s) (NLP):** Primarily English
+- **License:** apache-2.0
+- **Finetuned from model:** [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+
+## Evaluation
+
+![](./ioi-evals.png)
+
+
+
+## Usage
+Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
+
+```python
+# pip install transformers
+# pip install accelerate
+import torch
+from transformers import pipeline
+pipe = pipeline("text-generation", model="open-r1/OlympicCoder-7B", torch_dtype=torch.bfloat16, device_map="auto")
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [
+    {"role": "user", "content": "Write a python program to calculate the 10th Fibonacci number"},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=8000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+#<|im_start|>user
+#Write a python program to calculate the 10th fibonacci number<|im_end|>
+#<|im_start|>assistant
+#<think>Okay, I need to write a Python program that calculates the 10th Fibonacci number. Hmm, the Fibonacci sequence starts with 0 and 1. Each subsequent number is the sum of the two preceding ones. So the sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. ...
+```
+
+> [!WARNING]
+> To ensure that the model consistently outputs a long chain-of-thought, we have edited the chat template to prefill the first assistant turn with a `<think>` token. As a result, the outputs from this model will not show the opening `<think>` token if you use the model's `generate()` method.  To apply reinforcement learning with a format reward, either prepend the `<think>` token to the model's completions or amend the chat template to remove the prefill. 
+
+## Training procedure
+### Training hyper-parameters
+
+The following hyperparameters were used during training:
+
+- dataset: open-r1/codeforces-cots
+- learning_rate: 4.0e-5
+- train_batch_size: 2
+- seed: 42
+- packing: false
+- distributed_type: deepspeed-zero-3
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine_with_min_lr
+- min_lr_rate: 0.1
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 10.0