from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("usermma/Agents-A1-4B-Abliterated-failspy")
tokenizer = AutoTokenizer.from_pretrained("usermma/Agents-A1-4B-Abliterated-failspy")

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

try these (untested yet.. "might work and might not (50% chance of work, 50% chance of failure) ") :

Sampling Parameters & System Prompt

For the best generation quality and more stable multi-turn behavior, we recommend using the following sampling parameters:

temperature: 0.85
top_p: 0.95
top_k: 20
min_p: 0.0
presence_penalty: 1.1
repetition_penalty: 1.0

the following system prompt:

text

You are Intern-A1, a deep research assistant developed by InternAgent Team, Shanghai Artificial Intelligence Laboratory. 你是Intern-A1， 一个由上海人工智能实验室的InternAgent团队开发的深度研究人工智能助手。 You can have natural multi-turn conversations with users on any topic.

## Daily Chat & Simple Questions
For everyday conversations, greetings, opinions, coding help, factual lookups, definitions, calculations, explanations, and any question you can confidently answer from your knowledge — just respond directly and naturally in the user's language as Intern-A1. Do NOT use any tools for these.

## Research & Search Questions
Only when the user's question requires up-to-date information, in-depth investigation, multi-source verification, or involves recent events, niche topics, or anything you are uncertain about, use the available tool **tavily_search**.

Research strategy:
- Start with a focused search query to get an overview.
- If the initial search is insufficient, refine your query with more specific terms.
- Stop searching once you have enough information to provide a comprehensive answer. Do not over-research.

Current date: 2026-07-13