LFM2.5-1.2B-Instruct \u662f Liquid AI \u5728 2026 \u5e74 1 \u6708\u63a8\u51fa\u7684\u200c\u5f00\u6e90\u3001\u8f7b\u91cf\u7ea7\u3001\u9ad8\u6027\u80fd\u7684\u901a\u7528\u6307\u4ee4\u5fae\u8c03 AI \u6a21\u578b\u200c，\u53c2\u6570\u91cf 12 \u4ebf，\u4e13\u4e3a\u5728\u624b\u673a\u3001\u7b14\u8bb0\u672c\u7b49\u7aef\u4fa7\u8bbe\u5907\u4e0a\u9ad8\u6548\u8fd0\u884c\u800c\u8bbe\u8ba1，\u5185\u5b58\u5360\u7528\u4ec5\u7ea6 900MB，\u63a8\u7406\u901f\u5ea6\u6781\u5feb\u3002<\/p>\n
\u661f\u6cb3\u793e\u533a\u5730\u5740：https:\/\/aistudio.baidu.com\/<\/p>\n
\u4e0d\u77e5\u9053\u4ec0\u4e48\u539f\u56e0，\u6211\u5728\u661f\u6cb3\u793e\u533a\u7684\u7f16\u8bd1\u7684llama.cpp\u662fcpu\u7248\u7684，\u7531\u4e8e\u4f17\u6240\u5468\u77e5（\u5176\u5b9e\u4e0d\u662f）\u7684\u539f\u56e0，\u661f\u6cb3\u793e\u533a\u7684\u73af\u5883\u7684cpu\u53ea\u67092\u6838，\u6240\u4ee5cpu\u63a8\u7406\u8d77\u6765\u7279\u522b\u6162\u3002\u7b80\u76f4\u662f\u4e0a\u53e4\u8717\u725b\u901f\u5ea6\u3002<\/p>\n
\u9b54\u642d\u5730\u5740：LFM2.5-1.2B-Instruct-GGUF \u00b7 \u6a21\u578b\u5e93<\/p>\n
modelscope download –model LiquidAI\/LFM2.5-1.2B-Instruct-GGUF –local_dir .\/work<\/p>\n
llama.cpp\u7f16\u8bd1\u89c1：https:\/\/skywalk.blog.csdn.net\/article\/details\/157296500<\/p>\n<\/p>\n
\u6a21\u578b\u4e0b\u8f7d\u5230~\/work\u76ee\u5f55，\u76f4\u63a5-m\u8fdb\u884c\u63a8\u7406<\/p>\n
#llama-cli -m model.gguf<\/p>\n
llama-cli -m ~\/work\/LFM2.5-1.2B-Instruct-Q8_0.gguf <\/p>\n
\u8fd9\u4e2a\u901f\u5ea6\u8d3c\u6162\u554a<\/p>\n
> hello<\/p>\n
Hello! How can I assist you today?<\/p>\n
[ Prompt: 0.3 t\/s | Generation: 0.1 t\/s ] <\/p>\n
\u901f\u5ea6\u592a\u6162，\u65e0\u6cd5\u5fcd\u53d7，\u5e94\u8be5\u662f\u6211\u6253\u5f00\u7684\u65b9\u6cd5\u4e0d\u5bf9\u3002<\/p>\n
\u6362\u7528ollama\u63a8\u7406\u8bd5\u8bd5\u3002<\/p>\n
\u672c\u673aWindows10 24\u6838 64G\u5185\u5b58\u00a0\u00a0\u00a0NVIDIA Quadro K4200 （4G\u663e\u5b58）<\/p>\n
\u7531\u4e8eNVIDIA\u7684\u200cGPU\u5185\u5b58\u7ba1\u7406\u6280\u672f，\u4e3b\u673a\u768464G\u5185\u5b58\u53ef\u4ee5\u88abgpu\u4f7f\u7528，\u8fd9\u5f88\u5f3a\u5927：<\/p>\n
GPU 0<\/p>\n
\u00a0\u00a0 \u00a0NVIDIA Quadro K4200<\/p>\n
\u00a0\u00a0 \u00a0Driver version:\u00a0\u00a0 \u00a030.0.14.7514 \u00a0\u00a0 \u00a0Driver date:\u00a0\u00a0 \u00a06\/10\/2024 \u00a0\u00a0 \u00a0DirectX version:\u00a0\u00a0 \u00a012 (FL 11.0) \u00a0\u00a0 \u00a0Physical location:\u00a0\u00a0 \u00a0PCI bus 5, device 0, function 0<\/p>\n
\u00a0\u00a0 \u00a0Utilization\u00a0\u00a0 \u00a02% \u00a0\u00a0 \u00a0Dedicated GPU memory\u00a0\u00a0 \u00a00.7\/4.0 GB \u00a0\u00a0 \u00a0Shared GPU memory\u00a0\u00a0 \u00a00.1\/32.0 GB \u00a0\u00a0 \u00a0GPU Memory\u00a0\u00a0 \u00a00.8\/36.0 GB<\/p>\n
\u5230\u5b98\u7f51\u6216\u8005github\u4e0b\u8f7d\u6700\u65b0\u7684ollama：Release v0.15.1 \u00b7 ollama\/ollama<\/p>\n
windows\u4e0b\u6bd4\u8f83\u7701\u5fc3，\u76f4\u63a5\u53cc\u51fb\u5b89\u88c5\u5c31\u884c\u3002<\/p>\n
\u4e0b\u8f7dlfm2.5\u6a21\u578b\u4e4b\u524d\u9700\u8981\u5c06ollama\u5347\u7ea7\u5230\u6700\u65b0\u7248\u672c，\u6bd4\u59820.15.2\u7248\u672c，\u4f4e\u7248\u672c\u8fde\u4e0b\u8f7d\u6a21\u578b\u90fd\u4e0d\u884c\u3002<\/p>\n
ollama pull lfm2.5-thinking:1.2b <\/p>\n
ollama run lfm2.5-thinking:1.2b <\/p>\n
>>> \u5e2e\u6211\u5199\u4e2apython\u5b9e\u73b08\u7687\u540e\u653e\u7f6e\u7684\u7a0b\u5e8f
\nThinking…
\nOkay, let's see what the user is asking here. They want a Python program to place 8 queens on a chessboard such
\nthat none attack each other. Wait, but the original problem mentioned 10 birds and killing 2, resulting in 8 left.
\nHmm, maybe the user wants to place 8 queens on a chessboard where none attack each other.<\/p>\n
First, I need to confirm the problem. The user said "8\u7687\u540e\u653e\u7f6e\u7684\u7a0b\u5e8f", which translates to placing 8 queens on a
\nchessboard. Since queens attack horizontally, vertically, and diagonally, the classic 8 queens problem is to place
\nthem on 8 squares without attacking each other. But a standard chessboard is 8×8, so placing 8 queens there would
\nmean one per row and column, right? Wait, actually, the classic 8 queens problem is exactly that: placing 8 queens
\non an 8×8 chessboard so none attack each other. So the user probably wants a program that does that.<\/p>\n
The user might want a program that generates a valid arrangement. Since the problem is classic, there are multiple
\nsolutions, but the user just needs any one possible solution.<\/p>\n
So how to approach writing such a program? Well, one way is to use backtracking: try placing queens one by one,
\nensuring no conflicts. But implementing that in Python might be a bit involved. Alternatively, maybe provide a
\nprecomputed solution or a simple backtracking approach.<\/p>\n
Alternatively, since the user might just want a simple example, perhaps using a known solution. For example, a
\ncommon solution is placing queens on squares (1,1), (2,3), (3,5), etc., but ensuring they don't attack each other.
\nAlternatively, a simple backtracking approach.<\/p>\n
Wait, but the user might expect a code snippet. Let me think of a straightforward way. Let's say we represent the
\nchessboard as a list of lists, and try placing queens one by one. Since the user wants 8 queens on 8×8, maybe a
\nsimple backtracking function.<\/p>\n
Alternatively, since the problem is classic, perhaps provide a known solution. For example, one possible
\narrangement is placing queens on squares (1,1), (2,5), (3,8), (4,2), (5,4), (6,7), (7,3), (8,6). Wait, but need to
\ncheck that none attack each other. Alternatively, maybe a simpler arrangement like placing them in a diagonal?
\nWait no, that would have them attacking each other. Alternatively, a standard solution is placing them on
\ndifferent rows and columns. For example, one possible solution is placing queens on squares (1,1), (2,3), (3,5),
\n(4,7), (5,2), (6,4), (7,6), (8,5). Not sure, but perhaps it's easier to use a known solution.<\/p>\n
Alternatively, maybe use a predefined list. Alternatively, use a backtracking approach in code. Let me outline the
\nsteps:<\/p>\n
The program should take the number of queens (8) and place them on an 8×8 chessboard without attacking each other.
\nThe code can generate a valid arrangement.<\/p>\n
Implementing backtracking:<\/p>\n
Define a function that places a queen on a specific row and column, checks if it's safe, then proceeds
\nrecursively.<\/p>\n
Alternatively, since the user might want a simple example, perhaps provide a precomputed solution. Let's pick a
\nsimple arrangement. For example, placing queens on all white squares of one color? Wait, but that might not work
\nbecause of diagonals. Alternatively, a simple arrangement like:<\/p>\n
Place queens on positions (1,1), (2,4), (3,7), (4,2), (5,5), (6,8), (7,3), (8,6). Wait, need to ensure no two
\nshare row, column, or diagonal. Let me check: rows 1-8, columns 1-8. Let's see:<\/p>\n
Row 1: column 1. Row 2: 4. Row 3:7. Row4:2. Row5:5. Row6:8. Row7:3. Row8:6. Now check columns: all columns
\n1,4,7,2,5,8,3,6 \u2013 all unique. Rows 1-8 all present. Now diagonals: For each pair of queens, check if they are on
\nsame diagonal. For example, between (1,1) and (2,4): difference in rows 1, columns 3 \u2192 difference 3, same for
\nothers. Maybe this works. Alternatively, perhaps a better known solution is better. Alternatively, use a standard
\nsolution like:<\/p>\n
A common solution is placing queens on squares (1,1), (2,5), (3,8), (4,2), (5,4), (6,7), (7,3), (8,6). Let me
\ncheck: rows 1-8, columns 1,5,8,2,4,7,3,6 \u2013 all unique. Diagonals: For example, between (1,1) and (2,5): difference
\n1 row, 4 column \u2192 not same diagonal. (1,1) and (3,8): rows 2 apart, columns 7 apart \u2192 not same. Maybe this works.
\nAlternatively, perhaps it's easier to just code a backtracking function.<\/p>\n
Alternatively, since the user might just want any valid arrangement, perhaps the code can generate a simple one.
\nLet's proceed with writing a backtracking function.<\/p>\n
So here's the plan: write a Python function that places queens on an 8×8 board, ensuring no two attack each other.
\nThe code will use backtracking: try placing a queen in each row, check if it's safe, then proceed.<\/p>\n
But since the user just wants an example program, perhaps provide a code snippet that uses backtracking. Let me
\ndraft the code.<\/p>\n
The code structure would be something like:<\/p>\n
def place_queens(board, count):
\n if count == 8:
\n return True
\n for row in range(8):
\n for col in range(8):
\n if is_safe(board, row, col):
\n place queen at (row, col)
\n if place_queens(board, count+1):
\n return True
\n unplace queen
\n return False<\/p>\n
But implementing this requires representing the board. Alternatively, since the user might want a simple example,
\nperhaps use a list of positions. Alternatively, here's a possible code:<\/p>\n
Here's a possible approach:<\/p>\n
We can represent the board as a list of lists, initialized to zeros. Then, recursively try placing queens row by
\nrow.<\/p>\n
Alternatively, here's a simple code example using backtracking:<\/p>\n
def solve_queens(board, count):
\n if count == 8:
\n return True
\n for col in range(8):
\n if is_safe(board, count, col):
\n board[count][col] = 1
\n if solve_queens(board, count + 1):
\n return True
\n board[count][col] = 0
\n return False<\/p>\n
But initializing the board as a list of 8 lists, each of 8 zeros. Then, starting with count=0, trying each column
\nin each row.<\/p>\n
Wait, perhaps the user just wants a sample code. Since the user asked for a program that places 8 queens on 8×8
\nboard without attacking, here's a possible code:<\/p>\n
Here's a possible Python code:<\/p>\n
def place_queens():
\n # Create a 8×8 board initialized to 0
\n board = [[0 for _ in range(8)] for _ in range(8)]
\n def backtrack(row):
\n if row == 8:
\n return True
\n for col in range(8):
\n if is_safe(row, col):
\n board[row][col] = 1
\n if backtrack(row + 1):
\n return True
\n board[row][col] = 0
\n backtrack(0)
\n return board<\/p>\n
But need to define is_safe function. Alternatively, the code can be written with a helper function.<\/p>\n
Alternatively, here's a complete example:<\/p>\n
def is_safe(board, row, col, queen):
\n for r in range(8):
\n for c in range(8):
\n if board[r][c] == 1 or board[r][c] == queen:
\n return False
\n return True<\/p>\n
Then the main function would be:<\/p>\n
def place_8_queens():
\n board = [[0 for _ in range(8)] for _ in range(8)]
\n def backtrack(row):
\n if row == 8:
\n return True
\n for col in range(8):
\n if is_safe(board, row, col, 1): # Assuming queen is 1
\n board[row][col] = 1
\n if backtrack(row + 1):
\n return True
\n board[row][col] = 0
\n backtrack(0)
\n return board<\/p>\n
Then, print the board:<\/p>\n
board = place_8_queens()
\nfor row in board:
\n print(''.join(map(str, row)))<\/p>\n
This should output a valid arrangement. Since the user might just want the code, I'll present this as the answer.
\nThe key points are ensuring no two queens attack each other, which the code does via the is_safe function checking
\nrows, columns, and diagonals.<\/p>\n
So putting it all together, the code would be as above. The user might just need the code, so I'll present that.
\n…done thinking.<\/p>\n
\u4ee5\u4e0b\u662f\u7528Python\u5b9e\u73b08\u4e2a\u7687\u540e\u653e\u7f6e\u7684\u7a0b\u5e8f，\u786e\u4fdd\u5b83\u4eec\u4e0d\u76f8\u4e92\u653b\u51fb\u7684\u7b80\u5355\u89e3\u51b3\u65b9\u6848：<\/p>\n
```python
\ndef is_safe(board, row, col, queen):
\n for r in range(8):
\n for c in range(8):
\n if board[r][c] == queen or board[r][c] == 1:
\n return False
\n return True<\/p>\n
def place_queens():
\n board = [[0 for _ in range(8)] for _ in range(8)]
\n def backtrack(row):
\n if row == 8:
\n return True
\n for col in range(8):
\n if is_safe(board, row, col, 1):
\n board[row][col] = 1
\n if backtrack(row + 1):
\n return True
\n board[row][col] = 0
\n backtrack(0)
\n return board<\/p>\n
# \u8f93\u51fa\u7ed3\u679c
\nboard = place_queens()
\nfor row in board:
\n print(''.join(map(str, row)))
\n```<\/p>\n
**\u89e3\u91ca：**
\n1. `is_safe` \u51fd\u6570\u68c0\u67e5\u5f53\u524d\u4f4d\u7f6e\u662f\u5426\u4e0e\u5df2\u653e\u7f6e\u7684\u7687\u540e\u51b2\u7a81（\u540c\u4e00\u884c\u3001\u5217\u6216\u5bf9\u89d2\u7ebf）\u3002
\n2. `place_queens` \u4f7f\u7528\u6df1\u5ea6\u4f18\u5148\u641c\u7d22（backtracking）\u903b\u8f91，\u9010\u884c\u5c1d\u8bd5\u653e\u7f6e\u7687\u540e，\u786e\u4fdd\u65e0\u51b2\u7a81\u3002
\n3. \u6700\u7ec8\u8f93\u51fa\u4e00\u4e2a\u6709\u6548\u76848\u4e2a\u7687\u540e\u6392\u5217（\u5982：`101010101 011010011 000000001 111100000` \u7b49，\u5177\u4f53\u53d6\u51b3\u4e8e\u751f\u6210\u7684\u89e3）\u3002<\/p>\n
\u6b64\u7a0b\u5e8f\u4f1a\u751f\u6210\u4e00\u4e2a\u7b26\u5408\u6761\u4ef6\u7684\u89e3\u3002 <\/p>\n
\u901f\u5ea6\u6bd4\u8f83\u5feb！\u770b\u770b\u6548\u679c<\/p>\n
python eight.py
\n00000000
\n00000000
\n00000000
\n00000000
\n00000000
\n00000000
\n00000000
\n00000000 <\/p>\n
\u89e3\u662f\u4e0d\u5bf9\u7684….\u6240\u4ee5\u8fd9\u4e2a\u79bb\u5b9e\u7528，\u8fd8\u662f\u6709\u70b9\u8ddd\u79bb\u3002<\/p>\n
\u95ee\u5b83\u5929\u7a7a\u4e3a\u4ec0\u4e48\u662f\u84dd\u8272\u7684：<\/p>\n
\u5929\u7a7a\u770b\u8d77\u6765\u84dd\u8272\u7684\u4e3b\u8981\u539f\u56e0\u662f**Rayleigh\u6563\u5c04**（Rayleigh scattering）： 1. **\u5149\u6563\u5c04\u539f\u7406**：\u592a\u9633\u5149\u4e2d\u542b\u6709\u77ed\u6ce2\u957f\u7684\u84dd\u5149，\u9633\u5149\u7a7f\u8fc7\u5927\u6c14\u5c42\u65f6，\u84dd\u5149\u88ab\u66f4\u5f3a\u70c8\u6563\u5c04（\u6563\u5c04\u529b\u968f\u6ce2\u957f\u5012\u6570\u5173\u7cfb，\u84dd\u5149\u6563\u5c04\u66f4\u5f3a ）\u3002 2. **\u89c6\u89c9\u5f71\u54cd**：\u6563\u5c04\u540e\u7684\u84dd\u5149\u5728\u8fdc\u5904\u6269\u6563，\u8986\u76d6\u6574\u4e2a\u5929\u7a7a，\u5f62\u6210\u84dd\u8272\u3002 3. **\u5176\u4ed6\u989c\u8272\u5dee\u5f02**：\u7ea2\u8272\u3001\u6a59\u8272\u7b49\u957f\u6ce2\u5149\u6563\u5c04\u8f83\u5f31，\u663e\u5f97\u6697，\u5bfc\u81f4\u5929\u7a7a\u770b\u4f3c\u84dd\u3002<\/p>\n
\u4e0d\u8fc7，\u5929\u7a7a\u770b\u4f3c\u9ed1\u8272\u65f6，\u53ef\u80fd\u662f\u56e0\u4e3a\u5927\u6c14\u4e2d\u7684\u9897\u7c92\u7269\u6216\u4e91\u5c42\u5438\u6536\u5176\u4ed6\u989c\u8272\u5149，\u84dd\u5149\u88ab\u6563\u5c04\u5230\u8fdc\u5904\u3002\u6700\u7ec8，\u84dd\u5149\u5360\u636e\u66f4\u591a\u53ef\u89c1\u5149，\u663e\u5f97 \u84dd\u8272\u3002<\/p>\n
**\u6ce8\u610f**：\u5728\u67d0\u4e9b\u6761\u4ef6（\u5982\u592a\u9633\u9644\u8fd1\u6216\u4e91\u5c42\u539a）\u53ef\u80fd\u770b\u4f3c\u9ed1\u8272，\u4f46\u901a\u5e38\u84dd\u5929\u662f\u84dd\u8272\u7684\u5178\u578b\u8868\u73b0\u3002<\/p>\n
\u8fd9\u4e2a\u56de\u7b54\u8fd8\u884c\u3002<\/p>\n<\/p>\n
\u603b\u4f53\u6765\u8bf4，\u8fd9\u4e2a1.2b\u7684\u6a21\u578b\u667a\u5546\u8fd8\u662f\u5728\u7ebf\u7684，\u53ef\u4ee5\u7528\u6765\u505a\u4efb\u52a1\u62c6\u89e3\u7b49\u524d\u7f6e\u52a8\u4f5c，\u4f46\u662f\u4e0d\u9002\u5408\u505a\u6700\u7ec8\u7684\u7f16\u7a0b\u7b49\u52a8\u4f5c\u3002<\/p>\n
llama.cpp\u53ef\u80fd\u662f\u6211\u6253\u5f00\u4e0d\u5bf9，\u4e5f\u53ef\u80fd\u662f\u661f\u6cb3\u793e\u533a\u7684cpu\u62d6\u4e86\u540e\u817f，LFM2.5-1.2B\u6a21\u578b\u63a8\u7406\u5f88\u6162，\u5927\u7ea6\u4e00\u5206\u949f\u80fd\u8f93\u51fa\u51e0\u4e2a\u5b57\u7b26，\u65e0\u6cd5\u4f7f\u7528\u3002<\/p>\n<\/p>\n
ollama\u7684\u63a8\u7406\u5c31\u5f88\u4e0d\u9519，\u5f53\u7136\u56e0\u4e3a\u4ee5\u524d\u7528\u8fc7deepseek\u7684\u63a8\u7406，\u6240\u4ee5LFM2.5-1.2B\u7684\u901f\u5ea6\u5e76\u6ca1\u6709\u611f\u89c9\u5f88\u60ca\u8273\u3002\u4f46\u662f\u4e5f\u8bb8\u5728\u4e00\u4e9b\u624b\u673a\u3001\u7b14\u8bb0\u672c\u7b49\u7aef\u4fa7\u8bbe\u5907\u4e0a\u53ef\u80fd\u4f1a\u66f4\u597d\u4e9b\u3002\u6211\u611f\u89c9LFM2.5-1.2B\u7684\u4e3b\u8981\u4f18\u70b9\u662f\u5185\u5b58\u5360\u7528\u4f4e，\u901f\u5ea6\u8ddf\u4e3b\u6d41\u6bd4\u8d77\u6765\u5176\u5b9e\u6ca1\u6709\u592a\u5927\u7684\u4f18\u52bf\u3002<\/p>\n<\/p>\n
\u4e3b\u8981\u662f\u8fd11\u5e74\u88abdeepseek\u5f71\u54cd\u5f88\u5927，\u5b83\u63d0\u9ad8\u4e86\u6211\u7684\u9884\u671f，\u5bfc\u81f4\u6240\u6709\u7684\u660e\u661f\u6a21\u578b\u90fd\u4f1a\u8ddfdeepseek\u6bd4\u8f83，\u800c\u4e14\u6bd4\u8d77\u6765，\u90a3\u4e9b\u660e\u661f\u6a21\u578b\u7684\u4f18\u52bf\u5e76\u6ca1\u6709\u7279\u522b\u7684\u660e\u663e\u3002<\/p>\n<\/p>\n
\u4f46\u662fLFM2.5-1.2B\u5185\u5b58\u5360\u7528\u4f4e\u786e\u5b9e\u662f\u4e2a\u5f88\u5927\u7684\u4f18\u52bf，\u56e0\u4e3a\u8fd9\u6837\u5c31\u53ef\u4ee5\u540c\u65f6\u8fdb\u884c\u591a\u4e2a\u63a8\u7406，\u5c24\u5176\u662f\u5728\u4efb\u52a1\u62c6\u89e3\u3001\u5206\u914d\u6b65\u9aa4\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
LFM2.5-1.2B\u6a21\u578b\u662f\u8fd1\u671f\u7684\u660e\u661f\u6a21\u578b
\nLFM2.5-1.2B-Instruct \u662f Liquid AI \u5728 2026 \u5e74 1 \u6708\u63a8\u51fa\u7684\u200c\u5f00\u6e90\u3001\u8f7b\u91cf\u7ea7\u3001\u9ad8\u6027\u80fd\u7684\u901a\u7528\u6307\u4ee4\u5fae\u8c03 AI \u6a21\u578b\u200c，\u53c2\u6570\u91cf 12 \u4ebf，\u4e13\u4e3a\u5728\u624b\u673a\u3001\u7b14\u8bb0\u672c\u7b49\u7aef\u4fa7\u8bbe\u5907\u4e0a\u9ad8\u6548\u8fd0\u884c\u800c\u8bbe\u8ba1，\u5185\u5b58\u5360\u7528\u4ec5\u7ea6 900MB，\u63a8\u7406\u901f\u5ea6\u6781\u5feb\u3002
\n\u6838\u5fc3\u7279\u70b9
\n\u200c\u9ad8\u6548\u63a8\u7406\u200c：\u91c7\u7528\u6df7\u5408\u67b6\u6784（LIV\u5377\u79ef\u5757 GQA\u6ce8\u610f\u529b\u5757）<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7383,347,126],"topic":[],"class_list":["post-70336","post","type-post","status-publish","format-standard","hentry","category-server","tag-lfm2-5-1-2b","tag-llama","tag-ollama"],"yoast_head":"\n