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update dependencies
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@lfunderburk
lfunderburk committed Nov 15, 2023
1 parent bb358f9 commit 8809a80
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Showing 5 changed files with 422 additions and 171 deletions.
62 changes: 42 additions & 20 deletions ch2/jupyter-notebooks/interacting-with-llms-haystack2.0.ipynb
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},
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"source": [
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},
{
"cell_type": "code",
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"prompt_template = \"\"\"\n",
" Given a question, you provide a short answer.\n",
" \\nQuestion: {{question}}\n",
" \\nAnswer:\n",
" \"\"\"\n"
"gpt = GPTGenerator(api_key=os.getenv(\"OPENAI_API_KEY\"), \n",
" model_name='gpt-4', \n",
" api_base_url = 'https://api.openai.com/v1',\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"{'replies': ['The capital of France is Paris.'],\n",
" 'metadata': [{'model': 'gpt-4-0613',\n",
" 'index': 0,\n",
" 'finish_reason': 'stop',\n",
" 'usage': {'prompt_tokens': 25,\n",
" 'completion_tokens': 7,\n",
" 'total_tokens': 32}}]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gpt = GPTGenerator(api_key=os.getenv(\"OPENAI_API_KEY\"), \n",
" model_name='gpt-4', \n",
" api_base_url = 'https://api.openai.com/v1',\n",
" )"
"gpt.run(\"You are a helpful assistant who provides answers to questions. What is the capital of France?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"gpt.run(\"Who lives in Paris?\")"
"prompt_template = \"\"\"\n",
" Given a question, you provide a short answer.\n",
" \\nQuestion: {{question}}\n",
" \\nAnswer:\n",
" \"\"\"\n"
]
},
{
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"metadata": {},
"outputs": [],
"source": [
"question = \"Who is the smartest man in the universe\"\n",
"question = \"You are a helpful assistant who provides answers to questions. What is the capital of France?\"\n",
"result = hf_pipeline.run(\n",
" {\n",
" \"prompt_builder\": {\"question\": question},\n",
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"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"from scripts.huggingfaceendpoints import InferenceEndpointAPI"
]
},
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"mistral_pipeline.draw( \"pipeline-diagrams/mistral-pipeline.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What is the capital of France?\"\n",
"question = \"What is the capital of Rome?\"\n",
"result = mistral_pipeline.run(\n",
" {\n",
" \"prompt_builder\": {\"question\": question},\n",
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131 changes: 40 additions & 91 deletions ch2/jupyter-notebooks/interacting-with-llms-hugging-face.ipynb
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},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2527b40cc0134ed2b7e5d27f804755f3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/199 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d853bb35ec4742e2830a39062de02785",
"version_major": 2,
"version_minor": 0
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"text/plain": [
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]
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"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "22d8e2038d064db79eed6d05c7b5cf8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)cial_tokens_map.json: 0%| | 0.00/85.0 [00:00<?, ?B/s]"
]
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"metadata": {},
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{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67c7cda044b5420ebb19be2e9a1d2a1e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/715 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "32ec5cd195004c8ea4d598a07cc2596e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
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},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/macpro/anaconda3/envs/llm-pipelines/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n",
" warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"'NoneType' object has no attribute 'cadam32bit_grad_fp32'\n"
]
}
],
"outputs": [],
"source": []
},
{
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"trainer.model.save_pretrained(peft_model_id) \n",
"tokenizer.save_pretrained(peft_model_id) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connecting to a model through inference end points: MistralAI\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import os \n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()\n",
"# Applicable only if your env file is stored two levels above the current directory\n",
"load_dotenv(\"./../../.env\")\n",
"\n",
"hugging_face_token_endpoint = os.getenv(\"mistral_hf_token\")\n",
"API_URL = \"https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1\"\n",
"headers = {\"Authorization\": f\"Bearer {hugging_face_token_endpoint}\"}\n",
"\n",
"def query(payload):\n",
"\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
"\treturn response.json()\n",
"\t\n",
"output = query({\n",
"\t\"inputs\": \"You are a helpful assistant who provides answers to questions. What is the capital of France?\",\n",
"})\n",
"\n",
"print(output)"
]
}
],
"metadata": {
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44 changes: 29 additions & 15 deletions ch2/jupyter-notebooks/interacting-with-llms-openai.ipynb
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},
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"execution_count": null,
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"outputs": [],
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},
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},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"role\": \"assistant\",\n",
" \"content\": \"Hello! I can definitely help you with that. You can find Colombian coffee in various places. One option is to check your local grocery store or coffee shop. They often carry Colombian coffee brands. Additionally, you can also consider checking online retailers that specialize in coffee. They usually have a wide selection of Colombian coffee available for purchase. Is there anything else I can assist you with?\"\n",
"}\n"
]
}
],
"outputs": [],
"source": [
"system_prompt = \"\"\" \n",
"You are a helpful assistant who is knowledgeable about coffee. You are helpful to users who are seeking information about coffee. \n",
Expand All @@ -101,6 +90,31 @@
"\n",
"print(completion.choices[0].message) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(completion.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"completion.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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