diff --git a/cohere_command.py b/cohere_command.py
new file mode 100644
index 0000000..baa8658
--- /dev/null
+++ b/cohere_command.py
@@ -0,0 +1,80 @@
+import os
+from multiprocessing.pool import ThreadPool
+from typing import Tuple
+
+import cohere
+import pandas as pd
+from pyrate_limiter import Duration, Limiter, MemoryListBucket, RequestRate
+from tqdm import tqdm
+
+minute_rate = RequestRate(10000, Duration.MINUTE)
+limiter = Limiter(minute_rate, bucket_class=MemoryListBucket)
+NUM_THREADS = 2 * os.cpu_count()  # 2 threads per cpu core is standard
+
+co = cohere.Client(os.environ["COHERE_API_KEY"])
+
+# Need to download files from here since there is a bug in huggingface's datasets library
+# - https://huggingface.co/datasets/RyokoAI/ShareGPT52K/blob/main/sg_90k_part1.json
+# - https://huggingface.co/datasets/RyokoAI/ShareGPT52K/blob/main/sg_90k_part2.json
+dataset = pd.read_json("gs://cohere-dev-central-1/amr/aya/sg_90k_part2.json")
+
+
+@limiter.ratelimit("blobheart", delay=True)
+def api_generation(inputs: Tuple[cohere.Client, str]):
+    co, id, prompt = inputs
+    try:
+        generation = co.generate(
+            model='command-nightly',
+            prompt=prompt,
+            max_tokens=300,
+            temperature=0.9,
+            truncate='END',
+        ).generations[0]
+    except cohere.CohereError as exception:
+        print(exception)
+        return {
+            'id': id,
+            'prompt': prompt,
+            'completion': f'BLOBHEART_EXCEPTION_ERROR_{exception}',
+        }
+    return {
+        'id': id,
+        'prompt': prompt,
+        'completion': generation.text,
+    }
+
+
+output = {
+    "id": [],
+    "prompt": [],
+    "completion": [],
+}
+
+pbar = tqdm(range(len(dataset)), total=len(dataset), desc="Dataset Examples", position=0)
+prompts = []
+for conv_id in pbar:
+
+    example = dataset.iloc[conv_id]
+    conversation = example['conversations']
+
+    chat_history = []
+    max_turns = len(conversation) // 2
+    conversation_id = None
+
+    for i in range(max_turns):
+        prompt = conversation[i * 2]['value']
+        prompts.append((co, example['id'], prompt.strip()))
+
+print(f"Total number of prompts: {len(prompts)}")
+with ThreadPool(NUM_THREADS) as pool:
+    generations = list(tqdm(pool.imap(api_generation, prompts), total=len(prompts)))
+
+# import ipdb; ipdb.set_trace()
+for generation in generations:
+    output["id"].append(generation['id'])
+    output["prompt"].append(generation['prompt'])
+    output["completion"].append(generation['completion'])
+
+# import ipdb; ipdb.set_trace()
+output = pd.DataFrame(output)
+output.to_json("./command_prompt_completion_part2.json")