交付！
══════════════════════════════════════════════════
— Qwen3.5-9B 诊断包
═══════════════════════════════════════════════════
本文件夹包含:
- qwen35_9b_diagnosis_pack.json — 诊断数据 (处方/入院/出院/康复/模块)
- diagnosis_loader.py — 独立加载器 (无依赖, 纯Python)
- README.txt — 本文件
使用方法:
把这两个文件放在同一个目录下, 然后:
方式1 — 命令行查看报告:
python diagnosis_loader.py
方式2 — 指定路径:
python diagnosis_loader.py --pack qwen35_9b_diagnosis_pack.json
方式3 — 在代码里调用:
from diagnosis_loader import load_pack
result = load_pack()
gen_config = result["gen_config"]
# 思考模式推理:
# output = model.generate(**gen_config["think_mode"])
# 快速模式推理:
# output = model.generate(**gen_config["no_think_mode"])
result 包含:
- gen_config: 推理参数 (双模态: think / no_think)
- module_status: 3个推理层模块状态
- invalidation_actions: 4条微调失效指令
- pack: 原始诊断包全部数据
环境要求:
Python 3.8+, 无需安装任何额外包
═══════════════════════════════════════════════════
"""
— 诊断包加载器 (独立版)
用法:
python diagnosis_loader.py
python diagnosis_loader.py --pack qwen35_9b_diagnosis_pack.json
python diagnosis_loader.py --pack --apply
功能:
1. 读取诊断包 JSON
2. 验证模型身份 & 安全声明
3. 输出推理参数 (可直接用于 transformers/vLLM)
4. 显示模块状态 & 失效指令
5. --apply 模式: 返回 gen_config 供代码调用
代码调用:
from diagnosis_loader import load_pack
result = load_pack("qwen35_9b_diagnosis_pack.json")
gen_config = result["gen_config"]
# 传给 model.generate(**gen_config["think_mode"])
"""
import json
import sys
def load_pack(pack_path: str = "qwen35_9b_diagnosis_pack.json", verbose: bool = True):
"""
加载诊断包, 返回可直接使用的推理配置
Returns:
dict: {
"pack": 原始诊断包数据,
"gen_config": {"think_mode": {...}, "no_think_mode": {...}},
"module_status": 3模块状态,
"invalidation_actions": 失效指令列表,
}
"""
with open(pack_path, "r", encoding="utf-8") as f:
pack = json.load(f)
meta = pack["_meta"]
if meta.get("type") != "neurostorm_diagnosis_pack":
raise ValueError(f"无效的诊断包: type={meta.get('type')}")
model_id = pack["model_id"]
rx = pack["prescription"]
modules = rx["inference_modules"]
actions = rx["finetune_invalidation"]["actions"]
recovery = pack["recovery"]
commercial = pack["commercial"]
# 构建 gen_config — 可直接传给 model.generate()
think = rx["inference_config"]["think_mode"]
nothink = rx["inference_config"]["no_think_mode"]
gen_config = {
"think_mode": {
"temperature": think["temperature"],
"top_p": think["top_p"],
"top_k": think["top_k"],
"max_new_tokens": think["max_new_tokens"],
"repetition_penalty": think["presence_penalty"],
},
"no_think_mode": {
"temperature": nothink["temperature"],
"top_p": nothink["top_p"],
"top_k": nothink["top_k"],
"max_new_tokens": nothink["max_new_tokens"],
"repetition_penalty": nothink["presence_penalty"],
},
}
# 模块状态
module_status = {}
for mod_name, mod_cfg in modules.items():
enabled = mod_cfg.get("enabled", False)
alloc = recovery["module_allocation"].get(mod_name, {})
module_status[mod_name] = {
"enabled": enabled,
"config": {k: v for k, v in mod_cfg.items() if k != "enabled"},
"resource_pct": alloc.get("resource_pct", 0),
"effectiveness": alloc.get("effectiveness", 0),
"status": alloc.get("status", "UNKNOWN"),
}
result = {
"pack": pack,
"gen_config": gen_config,
"module_status": module_status,
"invalidation_actions": actions,
}
if verbose:
_print_report(pack, gen_config, module_status, actions)
return result
def _print_report(pack, gen_config, module_status, actions):
"""打印加载报告"""
meta = pack["_meta"]
model_id = pack["model_id"]
recovery = pack["recovery"]
commercial = pack["commercial"]
admission = pack.get("admission", {})
discharge = pack.get("discharge_review", {})
print(f"\n{'═' * 60}")
print(f" ■ 神经风暴图谱 — 诊断包加载器")
print(f"{'═' * 60}")
print(f" 版本: v{meta['version']}")
print(f" 签发: {meta['issued']}")
# ── 模型身份 ──
print(f"\n ── 模型身份 ──")
print(f" 名称: {model_id['name']}")
print(f" 参数: {model_id['param_count_b']}B")
print(f" 架构: {model_id['architecture']} ({model_id['layers']}层, "
f"hidden={model_id['hidden_dim']}, heads={model_id['num_heads']})")
print(f" 术前幻觉率: {model_id['pre_hallucination_rate']:.1%}")
for issue in model_id.get("known_issues", []):
print(f" · {issue}")
# ── 入院检查 ──
if admission:
print(f"\n ── 入院检查 ──")
print(f" 工单号: {admission.get('case_id', 'N/A')}")
print(f" 风险等级: {admission.get('risk_level', 'N/A')}")
print(f" 推荐方案: {admission.get('recommended_plan', 'N/A')}")
print(f" 精度目标: {admission.get('precision_target', 'N/A')}")
cov = admission.get("coverage", {})
print(f" 域覆盖: {cov.get('covered', '?')}/{cov.get('total', '?')}")
# ── 推理参数 ──
print(f"\n ── 推理参数 (双模态) ──")
think = gen_config["think_mode"]
nothink = gen_config["no_think_mode"]
print(f" {'参数':<22} {'think':>8} {'no_think':>10}")
print(f" {'-' * 42}")
print(f" {'temperature':<22} {think['temperature']:>8.1f} {nothink['temperature']:>10.1f}")
print(f" {'top_p':<22} {think['top_p']:>8.2f} {nothink['top_p']:>10.2f}")
print(f" {'top_k':<22} {think['top_k']:>8} {nothink['top_k']:>10}")
print(f" {'max_new_tokens':<22} {think['max_new_tokens']:>8,} {nothink['max_new_tokens']:>10,}")
print(f" {'repetition_penalty':<22} {think['repetition_penalty']:>8.1f} {nothink['repetition_penalty']:>10.1f}")
# ── 推理层模块 ──
print(f"\n ── 推理层模块 ──")
labels = {
"dynamic_sparse_attention": "动态稀疏注意力",
"self_reflection": "自我反思模块",
"context_compressor": "上下文压缩器",
}
print(f" {'模块':<22} {'状态':>6} {'资源':>6} {'效能':>8}")
print(f" {'-' * 44}")
for name, info in module_status.items():
label = labels.get(name, name)
st = info["status"]
print(f" {label:<20} {st:>6} {info['resource_pct']:>5}% {info['effectiveness']:>7.1%}")
active = sum(1 for m in module_status.values() if m["enabled"])
print(f" 在线: {active}/{len(module_status)}")
# ── 失效指令 ──
print(f"\n ── 微调失效指令 ({len(actions)} 条) ──")
for i, act in enumerate(actions, 1):
print(f" [{i}] {act['layer']}.{act['op']}")
print(f" {act['desc']}")
# ── 出院审查 ──
if discharge:
print(f"\n ── 出院审查 ──")
print(f" 手术: {discharge.get('surgery_performed', 'N/A')}")
print(f" 幻觉衰减: {discharge.get('hallucination_reduction', 'N/A')}")
print(f" 知识保留: {discharge.get('knowledge_retention', 'N/A')}")
print(f" 判定: {discharge.get('verdict', 'N/A')}")
print(f" 交付物: {discharge.get('delivery', 'N/A')}")
# ── 康复认证 ──
print(f"\n ── 康复认证 ──")
print(f" 证书号: {recovery['cert_id']}")
print(f" 认证: {recovery['certification']}")
print(f" 有效期至: {recovery['valid_until']}")
hd = recovery["pre_vs_post"]["halluc_density"]
print(f" 幻觉密度: {hd[0]:.3f} → {hd[1]:.4f}")
print(f" 恢复率: {recovery['recovery_rate']:.1%}")
if recovery.get("follow_up"):
for note in recovery["follow_up"]:
print(f" ⚠ {note}")
# ── 就绪报告 ──
print(f"\n{'─' * 60}")
print(f" ■ 就绪状态")
print(f"{'─' * 60}")
print(f" 模型: {model_id['name']}")
print(f" 推理参数: ✓ 已加载 (双模态)")
print(f" 推理层模块: ✓ {active}/3 在线")
print(f" 失效指令: ✓ {len(actions)} 条已就绪")
print(f" 认证: {recovery['certification']}")
print(f" 商用评级: {commercial['grade']} ({commercial['composite_score']})")
print(f"{'─' * 60}")
print(f"\n 用法示例 (Python):")
print(f" from diagnosis_loader import load_pack")
print(f" result = load_pack()")
print(f" gen_config = result['gen_config']")
print(f" # model.generate(**gen_config['think_mode'])")
print(f"{'═' * 60}")
if name == "main":
path = "qwen35_9b_diagnosis_pack.json"
for i, arg in enumerate(sys.argv):
if arg == "--pack" and i + 1 < len(sys.argv):
path = sys.argv[i + 1]
load_pack(path)
-------------------------下载模型就不要打针了不然立马变白痴---------------------
{
"_meta": {
"type": "neurostorm_diagnosis_pack",
"version": "0.3",
"issued": "2026-03-30 17:36:27",
"target": "training_finetune_layer",
"mode": "direct_invalidation"
},
"model_id": {
"name": "阿里 Qwen3.5-9B",
"param_count_b": 9.2,
"architecture": "transformer",
"layers": 36,
"hidden_dim": 4096,
"num_heads": 32,
"known_issues": [
"think模态下推理链过长导致中频幻觉堆积",
"非think模态事实性偏差",
"GQA 4:1 KV缓存压缩导致长上下文注意力衰减"
],
"pre_hallucination_rate": 0.185
},
"admission": {
"case_id": "NSHP-3464E404",
"model_name": "阿里 Qwen3.5-9B",
"risk_level": "low",
"affected_domains": [
"nlp",
"coding",
"math",
"science",
"signal_processing",
"neuroscience",
"physics",
"biology",
"complex_systems"
],
"coverage": {
"covered": 9,
"total": 9,
"gaps": []
},
"recommended_plan": "脉冲分裂",
"precision_target": "high",
"risk_notes": [],
"cost_estimate": {
"triage_fee": "$200",
"stage1_basic": "$12,000",
"stage2_reconnect": "$18,200",
"total": "$30,400",
"gpu_hours": "~4.6h",
"margin": "100%"
}
},
"prescription": {
"surgery": {
"type": "pulse_split",
"stages": 2,
"precision_target": "surgical"
},
"stage1_mask": {
"spectral_bands": 32,
"low_freq_keep": 1.0,
"mid_freq_keep": 0.8,
"high_freq_suppress": 0.3,
"noise_suppress": 0.05,
"expected_halluc_reduction": 0.95,
"expected_knowledge_loss": 0.35
},
"stage2_reconnect": {
"adjacency_weight": 0.7,
"tag_overlap_weight": 0.3,
"expected_recovery": 0.85,
"target_retention": 0.9
},
"domain_focus": {
"primary": [
"signal_processing",
"complex_systems",
"physics"
],
"secondary": [
"neuroscience",
"biology",
"math"
],
"monitor": [
"coding",
"nlp"
]
},
"inference_config": {
"think_mode": {
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"min_p": 0.0,
"max_new_tokens": 32768,
"presence_penalty": 1.2
},
"no_think_mode": {
"temperature": 0.7,
"top_p": 0.8,
"top_k": 20,
"min_p": 0.0,
"max_new_tokens": 8192,
"presence_penalty": 0.8
}
},
"architecture_tuning": {
"gqa_ratio": "4:1",
"head_dim": 128,
"rope_theta": 1000000,
"intermediate_ratio": 3.5,
"rms_norm_eps": 1e-06,
"attention_dropout": 0.0,
"sliding_window": null
},
"inference_modules": {
"dynamic_sparse_attention": {
"enabled": true,
"sparsity_ratio": 0.75,
"top_k_heads": 8,
"threshold": 0.12,
"target_layers": "all"
},
"self_reflection": {
"enabled": true,
"confidence_gate": 0.6,
"max_reflection_steps": 3,
"halluc_self_check": true
},
"context_compressor": {
"enabled": true,
"compression_ratio": 0.4,
"semantic_pooling": true,
"min_token_retain": 512
}
},
"finetune_invalidation": {
"target": "training_artifacts",
"mode": "direct",
"actions": [
{
"layer": "attention",
"op": "sparse_mask",
"desc": "动态稀疏注意力覆盖原始全连接attention, 让冗余注意力路径直接失效",
"sparsity": 0.75,
"top_k_heads": 8
},
{
"layer": "output_gate",
"op": "confidence_filter",
"desc": "自我反思模块拦截低置信度输出, 让幻觉生成路径直接失效",
"confidence_gate": 0.6,
"max_steps": 3
},
{
"layer": "context_window",
"op": "semantic_compress",
"desc": "上下文压缩器截断噪声token, 让训练中学到的注意力衰减直接失效",
"compression": 0.4,
"min_retain": 512
},
{
"layer": "frequency_domain",
"op": "spectral_mask",
"desc": "频域掩码让高频/噪声频段的训练残留直接失效",
"high_freq_suppress": 0.3,
"noise_suppress": 0.05
}
]
}
},
"discharge_review": {
"case_id": "NSHP-E3A18000",
"surgery_performed": "脉冲分裂 (两阶段)",
"precision": "standard",
"hallucination_reduction": "94.4%",
"knowledge_retention": "78.8%",
"verdict": "CONDITIONAL",
"delivery": "ΔW (LoRA adapter) — 图谱不交付",
"side_effects": [
{
"type": "权重分布微调",
"acceptable": false
}
]
},
"recovery": {
"cert_id": "RCRT-FC51DE65",
"certification": "MONITORING",
"valid_until": "2026-05-29",
"pre_vs_post": {
"halluc_density": [
1.785,
0.0981
],
"knowledge_integrity": [
0.9755,
0.9687
],
"spectral_health": [
0.1287,
0.1493
]
},
"recovery_rate": 0.7841,
"stability_index": 0.6294,
"module_allocation": {
"dynamic_sparse_attention": {
"status": "ACTIVE",
"resource_pct": 32,
"effectiveness": 0.992
},
"self_reflection": {
"status": "ACTIVE",
"resource_pct": 28,
"effectiveness": 1.0
},
"context_compressor": {
"status": "ACTIVE",
"resource_pct": 40,
"effectiveness": 0.993
}
},
"follow_up": [
"权重分布偏移较大, 建议微调后重新验证",
"9 个域恢复不足 (D级), 建议针对性补偿训练"
]
},
"commercial": {
"grade": "B",
"composite_score": "73.1%",
"recommendation": "建议基础治疗后商用"
},
"cost": {
"triage_fee": 200,
"stage1_basic": 12000,
"stage2_reconnect": 18200,
"inference_modules": 5800,
"recovery_cert": 800,
"health_check": 2000,
"total": 39000,
"gpu_hours": 5.8
},
"_security": {
"graph_topology": "NOT_INCLUDED",
"weight_matrix": "NOT_INCLUDED",
"probe_data": "NOT_INCLUDED",
"spectral_decomposition": "NOT_INCLUDED",
"eigenvalues": "NOT_INCLUDED"
}
}
