| Name | llama-index-llms-optimum-intel JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | llama-index llms optimum intel integration |
| upload_time | 2024-10-08 22:35:42 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
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# LlamaIndex Llms Integration: Optimum Intel IPEX backend
## Installation
To install the required packages, run:
```bash
%pip install llama-index-llms-optimum-intel
!pip install llama-index
```
## Setup
### Define Functions for Prompt Handling
You will need functions to convert messages and completions into prompts:
```python
from llama_index.llms.optimum_intel import OptimumIntelLLM
def messages_to_prompt(messages):
prompt = ""
for message in messages:
if message.role == "system":
prompt += f"<|system|>\n{message.content}</s>\n"
elif message.role == "user":
prompt += f"<|user|>\n{message.content}</s>\n"
elif message.role == "assistant":
prompt += f"<|assistant|>\n{message.content}</s>\n"
# Ensure we start with a system prompt, insert blank if needed
if not prompt.startswith("<|system|>\n"):
prompt = "<|system|>\n</s>\n" + prompt
# Add final assistant prompt
prompt = prompt + "<|assistant|>\n"
return prompt
def completion_to_prompt(completion):
return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
```
### Model Loading
Models can be loaded by specifying parameters using the `OptimumIntelLLM` method:
```python
oi_llm = OptimumIntelLLM(
model_name="Intel/neural-chat-7b-v3-3",
tokenizer_name="Intel/neural-chat-7b-v3-3",
context_window=3900,
max_new_tokens=256,
generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
device_map="cpu",
)
response = oi_llm.complete("What is the meaning of life?")
print(str(response))
```
### Streaming Responses
To use the streaming capabilities, you can use the `stream_complete` and `stream_chat` methods:
#### Using `stream_complete`
```python
response = oi_llm.stream_complete("Who is Mother Teresa?")
for r in response:
print(r.delta, end="")
```
#### Using `stream_chat`
```python
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system",
content="You are an American chef in a small restaurant in New Orleans",
),
ChatMessage(role="user", content="What is your dish of the day?"),
]
resp = oi_llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
```
### LLM Implementation example
https://docs.llamaindex.ai/en/stable/examples/llm/optimum_intel/
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"description": "# LlamaIndex Llms Integration: Optimum Intel IPEX backend\n\n## Installation\n\nTo install the required packages, run:\n\n```bash\n%pip install llama-index-llms-optimum-intel\n!pip install llama-index\n```\n\n## Setup\n\n### Define Functions for Prompt Handling\n\nYou will need functions to convert messages and completions into prompts:\n\n```python\nfrom llama_index.llms.optimum_intel import OptimumIntelLLM\n\n\ndef messages_to_prompt(messages):\n prompt = \"\"\n for message in messages:\n if message.role == \"system\":\n prompt += f\"<|system|>\\n{message.content}</s>\\n\"\n elif message.role == \"user\":\n prompt += f\"<|user|>\\n{message.content}</s>\\n\"\n elif message.role == \"assistant\":\n prompt += f\"<|assistant|>\\n{message.content}</s>\\n\"\n\n # Ensure we start with a system prompt, insert blank if needed\n if not prompt.startswith(\"<|system|>\\n\"):\n prompt = \"<|system|>\\n</s>\\n\" + prompt\n\n # Add final assistant prompt\n prompt = prompt + \"<|assistant|>\\n\"\n\n return prompt\n\n\ndef completion_to_prompt(completion):\n return f\"<|system|>\\n</s>\\n<|user|>\\n{completion}</s>\\n<|assistant|>\\n\"\n```\n\n### Model Loading\n\nModels can be loaded by specifying parameters using the `OptimumIntelLLM` method:\n\n```python\noi_llm = OptimumIntelLLM(\n model_name=\"Intel/neural-chat-7b-v3-3\",\n tokenizer_name=\"Intel/neural-chat-7b-v3-3\",\n context_window=3900,\n max_new_tokens=256,\n generate_kwargs={\"temperature\": 0.7, \"top_k\": 50, \"top_p\": 0.95},\n messages_to_prompt=messages_to_prompt,\n completion_to_prompt=completion_to_prompt,\n device_map=\"cpu\",\n)\n\nresponse = oi_llm.complete(\"What is the meaning of life?\")\nprint(str(response))\n```\n\n### Streaming Responses\n\nTo use the streaming capabilities, you can use the `stream_complete` and `stream_chat` methods:\n\n#### Using `stream_complete`\n\n```python\nresponse = oi_llm.stream_complete(\"Who is Mother Teresa?\")\nfor r in response:\n print(r.delta, end=\"\")\n```\n\n#### Using `stream_chat`\n\n```python\nfrom llama_index.core.llms import ChatMessage\n\nmessages = [\n ChatMessage(\n role=\"system\",\n content=\"You are an American chef in a small restaurant in New Orleans\",\n ),\n ChatMessage(role=\"user\", content=\"What is your dish of the day?\"),\n]\n\nresp = oi_llm.stream_chat(messages)\n\nfor r in resp:\n print(r.delta, end=\"\")\n```\n\n### LLM Implementation example\n\nhttps://docs.llamaindex.ai/en/stable/examples/llm/optimum_intel/\n",
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