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Model: RthItalia/NanoLLM-Qwen2.5-7B-v3.1
Source: Original Platform

.gitattributes

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LICENSE

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README.md

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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.

config.json

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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
+}

generation_config.json

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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"
+}

load_artifact.py

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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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quantized_modules.pt

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+size 1099712002

spec.json

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+}