Downloads · 30 days
64
7% of all-time downloads
RichardErkhov/pmking27_-_PrathameshLLM-2B-gguf
pmking27_-_PrathameshLLM-2B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
64
7% of all-time downloads
All-time downloads
979
Public
Repo size
30.7 GB
Likes
0
Public
Click a slice to open those files.
.gguf35.6 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
PrathameshLLM-2B - GGUF
| Name | Quant method | Size |
|---|---|---|
| PrathameshLLM-2B.Q2_K.gguf | Q2_K | 1.08GB |
| PrathameshLLM-2B.IQ3_XS.gguf | IQ3_XS | 1.16GB |
| PrathameshLLM-2B.IQ3_S.gguf | IQ3_S | 1.2GB |
| PrathameshLLM-2B.Q3_K_S.gguf | Q3_K_S | 1.2GB |
| PrathameshLLM-2B.IQ3_M.gguf | IQ3_M | 1.22GB |
| PrathameshLLM-2B.Q3_K.gguf | Q3_K | 1.29GB |
| PrathameshLLM-2B.Q3_K_M.gguf | Q3_K_M | 1.29GB |
| PrathameshLLM-2B.Q3_K_L.gguf | Q3_K_L | 1.36GB |
| PrathameshLLM-2B.IQ4_XS.gguf | IQ4_XS | 1.4GB |
| PrathameshLLM-2B.Q4_0.gguf | Q4_0 | 1.44GB |
| PrathameshLLM-2B.IQ4_NL.gguf | IQ4_NL | 1.45GB |
| PrathameshLLM-2B.Q4_K_S.gguf | Q4_K_S | 1.45GB |
| PrathameshLLM-2B.Q4_K.gguf | Q4_K | 1.52GB |
| PrathameshLLM-2B.Q4_K_M.gguf | Q4_K_M | 1.52GB |
| PrathameshLLM-2B.Q4_1.gguf | Q4_1 | 1.56GB |
| PrathameshLLM-2B.Q5_0.gguf | Q5_0 | 1.68GB |
| PrathameshLLM-2B.Q5_K_S.gguf | Q5_K_S | 1.68GB |
| PrathameshLLM-2B.Q5_K.gguf | Q5_K | 1.71GB |
| PrathameshLLM-2B.Q5_K_M.gguf | Q5_K_M | 1.71GB |
| PrathameshLLM-2B.Q5_1.gguf | Q5_1 | 1.79GB |
| PrathameshLLM-2B.Q6_K.gguf | Q6_K | 1.92GB |
| PrathameshLLM-2B.Q8_0.gguf | Q8_0 | 2.49GB |
language:
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
# Importing necessary modules
from transformers import AutoModelForCausalLM, AutoTokenizer
# Setting the device to load the model onto (assuming GPU availability)
device = 'cuda'
# Loading the tokenizer for the model
tokenizer = AutoTokenizer.from_pretrained("pmking27/PrathameshLLM-2B")
# Loading the pre-trained model
model = AutoModelForCausalLM.from_pretrained("pmking27/PrathameshLLM-2B")
# Defining the Alpaca prompt template
alpaca_prompt = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
# Providing the input to the model
model_inputs = tokenizer(
[
alpaca_prompt.format(
'''
You're an assistant trained to answer questions using the given context.
context:
General elections will be held in India from 19 April 2024 to 1 June 2024 to elect the 543 members of the 18th Lok Sabha. The elections will be held in seven phases and the results will be announced on 4 June 2024. This will be the largest-ever election in the world, surpassing the 2019 Indian general election, and will be the longest-held general elections in India with a total span of 44 days (excluding the first 1951–52 Indian general election). The incumbent prime minister Narendra Modi who completed a second term will be contesting elections for a third consecutive term.
Approximately 960 million individuals out of a population of 1.4 billion are eligible to participate in the elections, which are expected to span a month for completion. The Legislative assembly elections in the states of Andhra Pradesh, Arunachal Pradesh, Odisha, and Sikkim will be held simultaneously with the general election, along with the by-elections for 35 seats among 16 states.
''', # instruction
"भारतातील सार्वत्रिक निवडणुका किती टप्प्यात पार पडतील?", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt")
# Moving model inputs to the specified device
model_inputs.to(device)
model.to(device)
# Generating responses from the model
outputs = model.generate(**model_inputs, max_new_tokens=100)
decoded_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Finding the start and end positions of the response
start_marker = "### Response:"
end_marker = "<eos>"
start_pos = decoded_output.find(start_marker) + len(start_marker)
end_pos = decoded_output.find(end_marker, start_pos)
# Extracting the response text
response_text = decoded_output[start_pos:end_pos].strip()
print(response_text)
भारतातील सार्वत्रिक निवडणुका 7 टप्प्यांमध्ये पार पडतील.