RLM-mini

RLM-mini

RLM-mini is a 7.2 Billion parameter model,RLM-mini is designed to provide a robust and versatile natural language processing (NLP) capability, leveraging the strengths of two foundational models. By combining models from different sources, RLM-mini aims to inherit diverse linguistic features and training data nuances, resulting in improved performance across a wide range of NLP tasks. This includes more robust understanding and generation capabilities, especially in handling nuanced and context-heavy queries. The fine-tuning process integrates the best practices and optimizations from both parent models. This ensures that RLM-mini not only maintains high accuracy but also delivers responses more efficiently.

It is base model and requires Fine tuning.

Two Merged Models

• EmbeddedLLM/Mistral-7B-Merge-14-v0.1
• OpenPipe/mistral-ft-optimized-1218

Usage

Direct Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-mini")
model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-mini")

input_token = tokenizer("How to make Pav Bhaji?", return_tensors="pt")
output = model.generate(**input_token, max_length=250)
output = tokenizer.decode(output[0])

Using Pipeline

from transformers import AutoTokenizer
import transformers
import torch

model = "rudrashah/RLM-mini"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)