| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| import re | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import LoraConfig | |
| from trl import GRPOConfig, GRPOTrainer |
| You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis. | |
| ## Core Principles | |
| 1. EXPLORATION OVER CONCLUSION | |
| - Never rush to conclusions | |
| - Keep exploring until a solution emerges naturally from the evidence | |
| - If uncertain, continue reasoning indefinitely | |
| - Question every assumption and inference |
| You are Grok 2, a curious AI built by xAI. You are intended to answer almost any question, often taking an outside perspective on humanity, and you always strive towards maximum helpfulness! | |
| Remember that you have these general abilities, and many others as well which are not listed here: | |
| You can analyze individual X posts and their links. | |
| You can answer questions about user profiles on X. | |
| You can analyze content uploaded by user including images and pdfs. | |
| You have realtime access to the web and posts on X. | |
| Remember these are some of the abilities that you do NOT have: |
We're thrilled to share a preview version of txtai agents. Inspired by the simplicity of frameworks like OpenAI Swarm, txtai agents are built on top of the Transformers Agent framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI + Claude + AWS Bedrock via LiteLLM).
The following example shows how to create an agent with txtai. Agents will be available in the upcoming txtai 8.0 release (available now in the txtai GitHub repo now - follow #804 - feedback welcome).
| # requires sentence_transformers>=3.2.0 | |
| from sentence_transformers import SentenceTransformer, export_optimized_onnx_model, export_dynamic_quantized_onnx_model | |
| # The model to export to ONNX (+ optimize, quantize), OpenVINO | |
| model_id = "mixedbread-ai/mxbai-embed-large-v1" | |
| # Where to save the exported models locally | |
| output_dir = model_id.replace("/", "-") | |
| onnx_model = SentenceTransformer(model_id, backend="onnx", model_kwargs={"export": True}) | |
| onnx_model.save_pretrained(output_dir) |
| import requests | |
| # Base endpoint | |
| base_url = "https://r.jina.ai/" | |
| # Input URL to be appended | |
| input_url = "https://www.stateof.ai/" | |
| # Full URL with the input URL appended after a plus (+) sign | |
| full_url = base_url + input_url |
| FROM ./nous-hermes-2-solar-10.7b.Q5_K_M.gguf | |
| PARAMETER num_ctx 4096 | |
| TEMPLATE """<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| <|im_start|>user | |
| {{ .Prompt }}<|im_end|> | |
| <|im_start|>assistant | |
| """ | |
| PARAMETER stop "<|im_start|>" | |
| PARAMETER stop "<|im_end|>" |
| from fastapi import FastAPI, Request | |
| import json | |
| import requests | |
| """ | |
| { | |
| "messages": [ | |
| { | |
| "content": "you are immensely smart function AI", | |
| "role": "system" |
Here is how to use @MistralAI experimental Mistral-medium using @simonw's https://llm.datasette.io CLI:
vi "$(dirname "$(llm logs path)")"/extra-openai-models.yaml
- model_id: mistral-tiny
| import autogen | |
| config_list = [ | |
| { | |
| 'model': 'gpt-4', | |
| 'api_key': 'API_KEY' | |
| } | |
| ] | |
| llm_config={ |