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https://github.com/ikawrakow/ik_llama.cpp.git
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154e0d75fc
* Merging mainline - WIP * Merging mainline - WIP AVX2 and CUDA appear to work. CUDA performance seems slightly (~1-2%) lower as it is so often the case with llama.cpp/ggml after some "improvements" have been made. * Merging mainline - fix Metal * Remove check --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
36 lines
971 B
Python
36 lines
971 B
Python
import asyncio
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import asyncio.threads
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import requests
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import numpy as np
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n = 8
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result = []
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async def requests_post_async(*args, **kwargs):
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return await asyncio.threads.to_thread(requests.post, *args, **kwargs)
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async def main():
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model_url = "http://127.0.0.1:6900"
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responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(
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url= f"{model_url}/embedding",
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json= {"content": str(0)*1024}
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) for i in range(n)])
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for response in responses:
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embedding = response.json()["embedding"]
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print(embedding[-8:])
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result.append(embedding)
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asyncio.run(main())
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# compute cosine similarity
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for i in range(n-1):
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for j in range(i+1, n):
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embedding1 = np.array(result[i])
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embedding2 = np.array(result[j])
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similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
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print(f"Similarity between {i} and {j}: {similarity:.2f}")
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