Image-Text-to-Text
Transformers
Safetensors
deepseek_v41
text-generation
nvfp4
fp4
deepseek
Mixture of Experts
multimodal
libertai
8-bit precision
fp8
Instructions to use LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4
- SGLang
How to use LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 with Docker Model Runner:
docker model run hf.co/LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4
Sync verify_transcode.py (streamed Engram + GPU option, verifier, synthetic engram test)
Browse files- verify_transcode.py +80 -0
verify_transcode.py
ADDED
|
@@ -0,0 +1,80 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""Prove the MXFP4->NVFP4 expert transcode is bit-exact, and measure the Engram FP4 loss.
|
| 3 |
+
|
| 4 |
+
Reconstructs values from BOTH representations and compares:
|
| 5 |
+
source : FP4_TABLE[nibble] * scale_e8m0
|
| 6 |
+
output : FP4_TABLE[nibble] * scale_e4m3 * global_f32
|
| 7 |
+
A single non-identical element fails the run.
|
| 8 |
+
"""
|
| 9 |
+
import json, os, sys
|
| 10 |
+
import torch
|
| 11 |
+
from safetensors import safe_open
|
| 12 |
+
|
| 13 |
+
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
| 14 |
+
from dsv41_fp4_stream import (FP4_TABLE, SRC_BLOCK, NVFP4_BLOCK, EXPERT_RE, ENGRAM_RE,
|
| 15 |
+
unpack_e2m1)
|
| 16 |
+
|
| 17 |
+
src_dir, out_dir, shard = sys.argv[1], sys.argv[2], sys.argv[3]
|
| 18 |
+
|
| 19 |
+
sf = safe_open(os.path.join(src_dir, shard), framework="pt")
|
| 20 |
+
of = safe_open(os.path.join(out_dir, shard), framework="pt")
|
| 21 |
+
src_keys, out_keys = set(sf.keys()), set(of.keys())
|
| 22 |
+
|
| 23 |
+
n_exp = n_bad = 0
|
| 24 |
+
max_abs = 0.0
|
| 25 |
+
checked_elems = 0
|
| 26 |
+
for name in sorted(src_keys):
|
| 27 |
+
if not EXPERT_RE.match(name):
|
| 28 |
+
continue
|
| 29 |
+
base = name[:-len(".weight")]
|
| 30 |
+
a = unpack_e2m1(sf.get_tensor(name))
|
| 31 |
+
sa = sf.get_tensor(base + ".scale").float()
|
| 32 |
+
ref = a.view(a.shape[0], -1, SRC_BLOCK) * sa.unsqueeze(-1)
|
| 33 |
+
ref = ref.reshape(a.shape)
|
| 34 |
+
|
| 35 |
+
b = unpack_e2m1(of.get_tensor(base + ".weight"))
|
| 36 |
+
sb = of.get_tensor(base + ".weight_scale").float()
|
| 37 |
+
g = of.get_tensor(base + ".weight_scale_2").float()
|
| 38 |
+
got = b.view(b.shape[0], -1, NVFP4_BLOCK) * (sb * g).unsqueeze(-1)
|
| 39 |
+
got = got.reshape(b.shape)
|
| 40 |
+
|
| 41 |
+
d = (ref - got).abs().max().item()
|
| 42 |
+
max_abs = max(max_abs, d)
|
| 43 |
+
if d != 0.0:
|
| 44 |
+
n_bad += 1
|
| 45 |
+
if n_bad <= 3:
|
| 46 |
+
print(f" MISMATCH {base} max|d|={d:.6g}")
|
| 47 |
+
n_exp += 1
|
| 48 |
+
checked_elems += ref.numel()
|
| 49 |
+
if n_exp >= 200: # 200 experts is plenty and keeps the check quick
|
| 50 |
+
break
|
| 51 |
+
|
| 52 |
+
print(f"[experts] {n_exp} weights checked ({checked_elems/1e6:.1f}M elements), "
|
| 53 |
+
f"mismatches={n_bad}, max|delta|={max_abs:.6g}")
|
| 54 |
+
|
| 55 |
+
n_eng = 0
|
| 56 |
+
for name in sorted(src_keys):
|
| 57 |
+
if not ENGRAM_RE.match(name):
|
| 58 |
+
continue
|
| 59 |
+
base = name[:-len(".weight")]
|
| 60 |
+
rows = 65536
|
| 61 |
+
w = sf.get_slice(name)[:rows]
|
| 62 |
+
s = sf.get_slice(base + ".scale")[:rows].float()
|
| 63 |
+
ref = w.float().view(rows, -1, SRC_BLOCK) * s.unsqueeze(-1)
|
| 64 |
+
|
| 65 |
+
b = unpack_e2m1(of.get_slice(base + ".weight")[:rows])
|
| 66 |
+
sb = of.get_slice(base + ".scale")[:rows].float()
|
| 67 |
+
got = b.view(rows, -1, SRC_BLOCK) * sb.unsqueeze(-1)
|
| 68 |
+
|
| 69 |
+
a2, b2 = ref.flatten(1), got.flatten(1)
|
| 70 |
+
num = (a2 * b2).sum(1); den = a2.norm(dim=1) * b2.norm(dim=1)
|
| 71 |
+
ok = den > 0
|
| 72 |
+
cos = (num[ok] / den[ok])
|
| 73 |
+
rel = ((a2 - b2).norm(dim=1) / a2.norm(dim=1).clamp(min=1e-30))[ok]
|
| 74 |
+
print(f"[engram] {base} rows={rows} cos mean={cos.mean():.6f} min={cos.min():.6f} "
|
| 75 |
+
f"rel-err mean={rel.mean():.4f}")
|
| 76 |
+
n_eng += 1
|
| 77 |
+
|
| 78 |
+
if n_eng == 0:
|
| 79 |
+
print("[engram] none in this shard")
|
| 80 |
+
print("RESULT:", "LOSSLESS" if n_bad == 0 and n_exp > 0 else ("FAIL" if n_bad else "no experts here"))
|