mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-16 12:32:06 +00:00
Update docs to use new usage in openai>1.0.0 (#14163)
### Description Use new [APIs](https://github.com/openai/openai-python/blob/main/api.md#finetuning) ### Twitter handle [lin_bob57617](https://twitter.com/lin_bob57617)
This commit is contained in:
Bob Lin
GitHub
parent
052e23be3e
commit
ac449f186b
@ -243,16 +243,16 @@
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" my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode(\"utf-8\"))\n",
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"\n",
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"my_file.seek(0)\n",
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"training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
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"training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\n",
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"\n",
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"# OpenAI audits each training file for compliance reasons.\n",
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"# This make take a few minutes\n",
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"status = openai.File.retrieve(training_file.id).status\n",
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"status = openai.files.retrieve(training_file.id).status\n",
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"start_time = time.time()\n",
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"while status != \"processed\":\n",
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" print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
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" time.sleep(5)\n",
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" status = openai.File.retrieve(training_file.id).status\n",
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" status = openai.files.retrieve(training_file.id).status\n",
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"print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
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]
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},
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@ -271,7 +271,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"job = openai.FineTuningJob.create(\n",
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"job = openai.fine_tuning.jobs.create(\n",
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" training_file=training_file.id,\n",
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" model=\"gpt-3.5-turbo\",\n",
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")"
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@ -300,12 +300,12 @@
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}
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],
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"source": [
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"status = openai.FineTuningJob.retrieve(job.id).status\n",
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"status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
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"start_time = time.time()\n",
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"while status != \"succeeded\":\n",
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" print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
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" time.sleep(5)\n",
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" job = openai.FineTuningJob.retrieve(job.id)\n",
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" job = openai.fine_tuning.jobs.retrieve(job.id)\n",
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" status = job.status"
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]
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},
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@ -416,7 +416,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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@ -123,7 +123,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 6,
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"id": "817bc077-c18a-473b-94a4-a7d810d583a8",
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"metadata": {},
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"outputs": [],
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@ -145,7 +145,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 7,
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"id": "9e5ac127-b094-4584-9159-5a6d3d7315c7",
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"metadata": {},
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"outputs": [],
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@ -166,7 +166,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "11d19e28-be49-4801-8065-1a58d13cd192",
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"metadata": {},
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"outputs": [
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@ -174,7 +174,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Status=[running]... 302.42s. 143.85s\r"
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"Status=[running]... 429.55s. 46.34s\r"
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]
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}
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],
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@ -190,20 +190,20 @@
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" my_file.write((json.dumps({\"messages\": dialog}) + \"\\n\").encode(\"utf-8\"))\n",
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"\n",
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"my_file.seek(0)\n",
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"training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
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"training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\n",
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"\n",
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"job = openai.FineTuningJob.create(\n",
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"job = openai.fine_tuning.jobs.create(\n",
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" training_file=training_file.id,\n",
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" model=\"gpt-3.5-turbo\",\n",
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")\n",
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"\n",
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"# Wait for the fine-tuning to complete (this may take some time)\n",
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"status = openai.FineTuningJob.retrieve(job.id).status\n",
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"status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
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"start_time = time.time()\n",
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"while status != \"succeeded\":\n",
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" print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
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" time.sleep(5)\n",
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" status = openai.FineTuningJob.retrieve(job.id).status\n",
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" status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
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"\n",
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"# Now your model is fine-tuned!"
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]
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@ -220,16 +220,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"id": "3f472ca4-fa9b-485d-bd37-8ce3c59c44db",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the fine-tuned model ID\n",
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"job = openai.FineTuningJob.retrieve(job.id)\n",
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"job = openai.fine_tuning.jobs.retrieve(job.id)\n",
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"model_id = job.fine_tuned_model\n",
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"\n",
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"# Use the fine-tuned model in LangChain\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(\n",
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" model=model_id,\n",
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" temperature=1,\n",
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@ -238,10 +240,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 11,
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"id": "7d3b5845-6385-42d1-9f7d-5ea798dc2cd9",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='[{\"s\": \"There were three ravens\", \"object\": \"tree\", \"relation\": \"sat on\"}, {\"s\": \"three ravens\", \"object\": \"a tree\", \"relation\": \"sat on\"}]')"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.invoke(\"There were three ravens sat on a tree.\")"
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]
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@ -271,7 +284,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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@ -35,7 +35,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "473adce5-c863-49e6-85c3-049e0ec2222e",
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"metadata": {},
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"outputs": [],
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@ -65,7 +65,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "9a36d27f-2f3b-4148-b94a-9436fe8b00e0",
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"metadata": {},
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"outputs": [],
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@ -105,7 +105,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "89bcc676-27e8-40dc-a4d6-92cf28e0db58",
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"metadata": {},
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"outputs": [
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@ -144,7 +144,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "cd44ff01-22cf-431a-8bf4-29a758d1fcff",
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"metadata": {},
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"outputs": [],
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@ -169,18 +169,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"id": "62da7d8f-5cfc-45a6-946e-2bcda2b0ba1f",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"math_questions = [\n",
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" \"What's 45/9?\",\n",
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@ -219,7 +211,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"id": "d6037992-050d-4ada-a061-860c124f0bf1",
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"metadata": {},
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"outputs": [],
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@ -231,7 +223,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"id": "0444919a-6f5a-4726-9916-4603b1420d0e",
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"metadata": {},
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"outputs": [],
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@ -266,7 +258,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"id": "817bc077-c18a-473b-94a4-a7d810d583a8",
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"metadata": {},
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"outputs": [],
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@ -288,7 +280,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"id": "9e5ac127-b094-4584-9159-5a6d3d7315c7",
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"metadata": {},
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"outputs": [],
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@ -309,7 +301,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"id": "11d19e28-be49-4801-8065-1a58d13cd192",
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"metadata": {},
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"outputs": [
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@ -317,7 +309,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Status=[running]... 346.26s. 31.70s\r"
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"Status=[running]... 349.84s. 17.72s\r"
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]
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}
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],
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@ -333,20 +325,20 @@
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" my_file.write((json.dumps({\"messages\": dialog}) + \"\\n\").encode(\"utf-8\"))\n",
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"\n",
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"my_file.seek(0)\n",
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"training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
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"training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\n",
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"\n",
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"job = openai.FineTuningJob.create(\n",
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"job = openai.fine_tuning.jobs.create(\n",
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" training_file=training_file.id,\n",
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" model=\"gpt-3.5-turbo\",\n",
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")\n",
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"\n",
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"# Wait for the fine-tuning to complete (this may take some time)\n",
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"status = openai.FineTuningJob.retrieve(job.id).status\n",
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"status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
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"start_time = time.time()\n",
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"while status != \"succeeded\":\n",
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" print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
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" time.sleep(5)\n",
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" status = openai.FineTuningJob.retrieve(job.id).status\n",
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" status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
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"\n",
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"# Now your model is fine-tuned!"
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]
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@ -363,16 +355,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"id": "7f45b281-1dfa-43cb-bd28-99fa7e9f45d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the fine-tuned model ID\n",
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"job = openai.FineTuningJob.retrieve(job.id)\n",
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"job = openai.fine_tuning.jobs.retrieve(job.id)\n",
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"model_id = job.fine_tuned_model\n",
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"\n",
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"# Use the fine-tuned model in LangChain\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(\n",
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" model=model_id,\n",
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" temperature=1,\n",
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@ -381,17 +375,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 12,
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"id": "7d3b5845-6385-42d1-9f7d-5ea798dc2cd9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='{\\n \"num1\": 56,\\n \"num2\": 7,\\n \"operation\": \"/\"\\n}')"
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"AIMessage(content='Let me calculate that for you.')"
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]
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},
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"execution_count": 18,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -425,7 +419,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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