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---
language:
- en
- zh
- it
license: other
tags:
- quantization
- qwen
- qwen2.5
- mixed-precision
- inference
library_name: transformers
pipeline_tag: text-generation
---
# NanoLLM Qwen v3.1
NanoLLM v3.1 artifacts are compact overlay artifacts for Qwen2.5 models. The loader starts from the base model in bitsandbytes 8-bit mode, then replaces the modules that passed the NanoLLM cascade with `TrueQuantLinear` modules.
## Validated Artifacts
| Model | Artifact | Zip size | Gate | Avg cosine | Min cosine | Locked / 8-bit pending |
| --- | --- | ---: | --- | ---: | ---: | ---: |
| Qwen2.5-3B-Instruct | `final_artifact_3B.zip` | 799,189,680 bytes | PASS | 0.990625 | 0.984375 | 143 / 109 |
| Qwen2.5-7B-Instruct | `final_artifact_7B.zip` | 891,419,698 bytes | PASS | 0.990625 | 0.98046875 | 66 / 130 |
| Qwen2.5-14B-Instruct | `final_artifact_Qwen2.5-14B-Instruct_pruned_pass.zip` | 1,482,019,132 bytes | PASS | 0.990625 | 0.98046875 | 76 / 260 |
The current release gate checks average next-token-logit cosine similarity against the 8-bit reference: `avg >= 0.99`. Minimum cosine is reported as a diagnostic.
## Quick Start
```python
from load_artifact import load_artifact
model, tokenizer, spec = load_artifact("final_artifact_Qwen2.5-14B-Instruct")
prompt = "Write a Python function to sort a list using bubble sort."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Requirements:
```bash
pip install torch transformers accelerate bitsandbytes safetensors
```
## Runtime Notes
- `build_reference_mode`: `8bit`
- `reference_scope`: `original_baseline`
- `pending_policy`: `leave_in_base_8bit`
- `NANO_LOAD_4BIT=1` can be used experimentally to load the base model in 4-bit, but the release tests use 8-bit.
## License
The NanoLLM quantization pipeline is proprietary/internal. Generated artifacts are published for research and evaluation subject to the repository license terms.

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.43.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"bos_token_id": 151643,
"pad_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_p": 0.8,
"top_k": 20,
"transformers_version": "4.37.0"
}

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"""Loader NANO-v3.1 UNIVERSAL (Inference Only)"""
import os
import json
from pathlib import Path
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
class TrueQuantLinear(nn.Module):
def __init__(self, pq, ps, pi, dq, ds, di, out_features, bias=None, bits=8, device="cuda:0"):
super().__init__()
self.out_features = out_features
self.bits = int(bits)
self.register_buffer("pq", pq.to(device=device, dtype=torch.int8))
self.register_buffer("ps", ps.to(device=device, dtype=torch.float16))
self.register_buffer("pi", pi.to(device=device, dtype=torch.long))
self.register_buffer("dq", dq.to(device=device, dtype=torch.int8))
self.register_buffer("ds", ds.to(device=device, dtype=torch.float16))
self.register_buffer("di", di.to(device=device, dtype=torch.long))
if bias is not None:
self.register_buffer("bias", bias.to(device=device, dtype=torch.float16))
else:
self.bias = None
def forward(self, x):
d, dt = x.device, x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
o = torch.zeros(f.shape[0], self.out_features, dtype=torch.float16, device=d)
if self.pq.shape[0] > 0:
o.index_copy_(-1, self.pi.to(d), f @ (self.pq.to(d, torch.float16) * self.ps.to(d).unsqueeze(1)).t())
if self.dq.shape[0] > 0:
o.index_copy_(-1, self.di.to(d), f @ (self.dq.to(d, torch.float16) * self.ds.to(d).unsqueeze(1)).t())
if self.bias is not None:
o = o + self.bias.to(d)
return o.reshape(*x.shape[:-1], self.out_features).to(dt)
def _set(root, name, value):
parts = name.split(".")
parent = root
for p in parts[:-1]:
parent = parent[int(p)] if p.isdigit() else getattr(parent, p)
if parts[-1].isdigit():
parent[int(parts[-1])] = value
else:
setattr(parent, parts[-1], value)
def get_module(root, name):
cur = root
for p in name.split("."):
cur = cur[int(p)] if p.isdigit() else getattr(cur, p)
return cur
def load_artifact(artifact_dir):
d = Path(artifact_dir)
spec = json.loads((d / "spec.json").read_text("utf-8"))
state = torch.load(d / "quantized_modules.pt", map_location="cpu")
use_4bit = os.getenv("NANO_LOAD_4BIT", "0").strip().lower() in {"1", "true", "yes", "on"}
qcfg = (
BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
if use_4bit
else BitsAndBytesConfig(load_in_8bit=True)
)
model = AutoModelForCausalLM.from_pretrained(
str(d),
quantization_config=qcfg,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(str(d), use_fast=True)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
for name, s in state.items():
dev = next(get_module(model, name).parameters()).device
bits = s["bits"]
if "deg_q_packed" in s:
pk, pad = s["deg_q_packed"], s["pad"]
if bits == 2:
dq = torch.stack([pk & 3, (pk >> 2) & 3, (pk >> 4) & 3, (pk >> 6) & 3], dim=-1).view(pk.shape[0], -1)
if pad > 0:
dq = dq[:, :-pad]
dq = dq.to(torch.int8) - 1
else:
dq = torch.stack([pk & 15, (pk >> 4) & 15], dim=-1).view(pk.shape[0], -1)
if pad > 0:
dq = dq[:, :-pad]
dq = dq.to(torch.int8) - 7
else:
dq = s.get("deg_q", torch.zeros(0, dtype=torch.int8))
_set(
model,
name,
TrueQuantLinear(
s["prot_q"],
s["prot_scale"],
s["prot_idx"],
dq,
s["deg_scale"],
s["deg_idx"],
s["out_features"],
s.get("bias"),
bits,
device=str(dev),
),
)
return model.eval(), tokenizer, spec

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