deepseek-v2-lite-fp8-topo2026

CODE: https://github.com/frank-morales2020/AST/blob/main/AAI_DEEPSEEK_TOPO.ipynb

ARTICLE: https://medium.com/ai-simplified-in-plain-english/agentic-ai-without-amnesia-the-topo-2026-revolution-dfee42ec55a5

Usage

# ============================================================================
# DEEPSEEK-V2-LITE FP8 — INFERENCE TEST
# ============================================================================
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
from unittest.mock import patch
import torch, transformers, logging

transformers.logging.set_verbosity_error()
logging.getLogger("transformers").setLevel(logging.ERROR)

MODEL_ID = 'frankmorales2020/deepseek-v2-lite-fp8-topo2026'
device   = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

_orig_init = PreTrainedModel._initialize_weights
def _safe_init(self, module):
    try: _orig_init(self, module)
    except NotImplementedError as e:
        if 'Float8_e4m3fn' in str(e) or 'normal_kernel_cpu' in str(e):
            module._is_hf_initialized = True
        else: raise

with patch.object(PreTrainedModel, '_initialize_weights', _safe_init):
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID, trust_remote_code=True, torch_dtype=torch.bfloat16,
        device_map='cuda:0', low_cpu_mem_usage=True,
    )
model.eval()

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
tokenizer.pad_token_id = tokenizer.eos_token_id

TEST_PROMPTS = [
    'The capital of France is',
    'In machine learning, catastrophic forgetting refers to',
    'DeepSeek-V2-Lite is a mixture-of-experts model that',
]

print('='*65)
print(f'DEEPSEEK-V2-LITE FP8 INFERENCE TEST | {MODEL_ID}')
print('='*65)
for prompt in TEST_PROMPTS:
    inp = tokenizer(prompt, return_tensors='pt').to(device)
    with torch.no_grad():
        out = model.generate(
            **inp, max_new_tokens=40, do_sample=False,
            use_cache=False, repetition_penalty=1.3,
            pad_token_id=tokenizer.eos_token_id,
        )
    response = tokenizer.decode(out[0][inp['input_ids'].shape[1]:], skip_special_tokens=True)
    print(f'\nPrompt : {prompt}')
    print(f'Output : {response}')
print('\n' + '='*65)

OUTPUT:

 Compressing model: 3463it [00:17, 195.25it/s]
Loading checkpoint shards: 100% 5/5 [00:02<00:00,  2.46it/s]=================================================================
DEEPSEEK-V2-LITE FP8 INFERENCE TEST | frankmorales2020/deepseek-v2-lite-fp8-topo2026
=================================================================

Prompt : The capital of France is
Output :  Paris.
Paris, the city that never sleeps! The French call it “La Ville-Lumière” (the City Of Light). It’s a place where you can find

Prompt : In machine learning, catastrophic forgetting refers to
Output :  the phenomenon where a model trained on multiple tasks or datasets forgets previously learned information when it is presented with new data. This can occur because each task requires different parameters and weights for optimal performance; if these

Prompt : DeepSeek-V2-Lite is a mixture-of-experts model that
Output :  can be used to generate text. It was trained on the Deepseek dataset, which contains over 10 billion tokens of English language data from various sources such as books and news articles.
The

=================================================================

# Run once, then RESTART RUNTIME, then run this cell:
#   !pip install -q "compressed-tensors>=0.15.0"

import warnings, logging, os
warnings.filterwarnings("ignore")
logging.getLogger("transformers").setLevel(logging.ERROR)
os.environ["TRANSFORMERS_VERBOSITY"] = "error"
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1"
try:
    from transformers.utils import logging as _hf_log
    _hf_log.set_verbosity_error()
except Exception:
    pass

import transformers.utils.import_utils as _iu
import transformers.utils as _tu
for _m in (_iu, _tu):
    if not hasattr(_m, "is_torch_fx_available"):
        _m.is_torch_fx_available = lambda: False

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers.modeling_utils as _mu

# FP8 fix: skip re-init of already-loaded FP8 weights.
_mu.PreTrainedModel._initialize_weights = lambda self, *a, **k: None
_mu.PreTrainedModel.initialize_weights  = lambda self, *a, **k: None

REPO = "frankmorales2020/deepseek-v2-lite-fp8-topo2026"
PROMPTS = ["What is the capital of Japan?",
           "What is the Spanish word for water?",
           "What is Einstein's mass-energy equivalence?"]

try:
    tok = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
except Exception:
    tok = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V2-Lite", trust_remote_code=True)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token

model = AutoModelForCausalLM.from_pretrained(
    REPO, dtype=torch.float16, device_map="auto",
    trust_remote_code=True, attn_implementation="eager").eval()

nan = torch.isnan(model.get_input_embeddings().weight.float()).any().item()
print("Embedding has NaN?", nan)

if not nan:
    for p in PROMPTS:
        ins = tok(p, return_tensors="pt").to(model.device)
        out = model.generate(**ins, max_new_tokens=40, do_sample=False,
                             repetition_penalty=1.2, no_repeat_ngram_size=3,
                             use_cache=False,
                             pad_token_id=tok.pad_token_id)
        txt = tok.decode(out[0], skip_special_tokens=True)
        print(f"\nQ: {p}\nA: {txt[len(p):].strip() if txt.startswith(p) else txt.strip()}")

OUTPUT-EXPECTED

Compressing model: 100%|██████████| 3463/3463 [00:01<00:00, 1977.29it/s]
Loading weights: 100% 12217/12217 [00:01<00:00, 8114.97it/s]Embedding has NaN? False
Decompressing model: 100%|██████████| 3463/3463 [00:00<00:00, 13103.20it/s]

Q: What is the capital of Japan?
A: The answer to this question may seem obvious, but it’s actually a bit more complicated than you might think. The country has two capitals: Tokyo and Kyoto. Both cities are important in Japanese

Q: What is the Spanish word for water?
A: The English translation of “agua” in a sentence. Agua means Water, and it’s pronounced like this: ah-gwah (like how you say ‘gwa’

Q: What is Einstein's mass-energy equivalence?
A: Einstein’s Mass Energy Equivalence states that the energy of a body at rest (E) equals its mass times the speed of light squared. This equation can be written as: E = mc