mirror of
https://github.com/rasbt/LLMs-from-scratch.git
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Introduce buffers to improve Llama 3.2 efficiency (#389)
* Introduce buffers to improve Llama 3.2 efficiency * update * update
This commit is contained in:
Sebastian Raschka
GitHub
parent
a0c0c765a8
commit
1eb0b3810a
@ -430,6 +430,14 @@
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"- In short, the main change in GQA is that each query group needs to be repeated to match the number of heads it is associated with, as implemented below"
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]
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},
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{
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"cell_type": "markdown",
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"id": "842aa71a-4659-424e-8830-392bd6ae86af",
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"metadata": {},
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"source": [
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"- In addition, we also introduce a `SharedBuffers` class that will allow us to reuse the `mask`, `cos`, and `sin` tensors in the transformer blocks to improve efficiency (this will be crucial when working with models such as Llama 3.1 and 3.2 later, which support up to 131k input tokens)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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@ -441,6 +449,28 @@
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"source": [
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"import torch.nn as nn\n",
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"\n",
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"\n",
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"############################# NEW #############################\n",
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"class SharedBuffers:\n",
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" _buffers = {}\n",
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"\n",
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" @staticmethod\n",
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" def get_buffers(context_length, head_dim, rope_base, freq_config, dtype=torch.float32):\n",
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" key = (context_length, head_dim, rope_base, tuple(freq_config.values()) if freq_config else freq_config, dtype)\n",
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"\n",
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" if key not in SharedBuffers._buffers:\n",
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" # Create or fetch the buffers\n",
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" mask = torch.triu(torch.ones(context_length, context_length), diagonal=1)\n",
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" cos, sin = precompute_rope_params(head_dim, rope_base, context_length, freq_config)\n",
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" if dtype is not None:\n",
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" cos = cos.to(dtype)\n",
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" sin = sin.to(dtype)\n",
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" SharedBuffers._buffers[key] = (mask, cos, sin)\n",
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"\n",
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" return SharedBuffers._buffers[key]\n",
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"############################# NEW #############################\n",
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"\n",
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"\n",
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"class GroupedQueryAttention(nn.Module):\n",
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" def __init__(\n",
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" self, d_in, d_out, context_length, num_heads,\n",
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@ -469,13 +499,12 @@
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" self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
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" self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)\n",
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"\n",
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" self.register_buffer(\"mask\", torch.triu(torch.ones(context_length, context_length), diagonal=1))\n",
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" cos, sin = precompute_rope_params(\n",
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" head_dim=self.head_dim,\n",
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" theta_base=rope_base, # NEW\n",
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" freq_config=rope_config, # NEW\n",
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" context_length=8192\n",
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" )\n",
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" ############################# NEW #############################\n",
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" # Fetch buffers using SharedBuffers\n",
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" mask, cos, sin = SharedBuffers.get_buffers(context_length, self.head_dim, rope_base, rope_config, dtype)\n",
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" ############################# NEW #############################\n",
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" \n",
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" self.register_buffer(\"mask\", mask)\n",
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" self.register_buffer(\"cos\", cos)\n",
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" self.register_buffer(\"sin\", sin)\n",
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"\n",
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@ -907,6 +936,35 @@
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"model = Llama3Model(LLAMA3_CONFIG_8B)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "edea6334-d1fc-427d-9cf2-4af963ff4bfc",
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"metadata": {},
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"source": [
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"- The following is expected to print True to confirm buffers are reused instead of being (wastefully) recreated:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ee9625cc-9afa-4b11-8aab-d536fd170761",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check buffers\n",
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"print(model.trf_blocks[0].att.mask is model.trf_blocks[-1].att.mask)\n",
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"print(model.trf_blocks[0].att.cos is model.trf_blocks[-1].att.cos)\n",
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"print(model.trf_blocks[0].att.sin is model.trf_blocks[-1].att.sin) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "8056a521-91a6-440f-8473-591409c3177b",
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"metadata": {},
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"source": [
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"- Let's now also compute the number of trainable parameters:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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@ -2008,16 +2066,16 @@
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"}\n",
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"\n",
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"LLAMA31_CONFIG_8B = {\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 8192, # Context length\n",
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" \"emb_dim\": 4096, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 32, # Number of layers\n",
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" \"hidden_dim\": 14_336, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # NEW: RoPE frequency scaling\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 131_072, # NEW: Larger supported context length\n",
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" \"emb_dim\": 4096, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 32, # Number of layers\n",
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" \"hidden_dim\": 14_336, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # NEW: RoPE frequency scaling\n",
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" \"factor\": 8.0,\n",
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" \"low_freq_factor\": 1.0,\n",
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" \"high_freq_factor\": 4.0,\n",
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@ -2026,6 +2084,24 @@
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d81ee464-c112-43b0-9ee8-70df6ac942d0",
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"metadata": {},
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"source": [
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"- Reduce the context length so the model would work fine on a MacBook Air (if you have more RAM, feel free to comment out the lines below):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9bdbe32f-4c96-4e60-8bf4-52b5217df1e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"LLAMA32_CONFIG[\"context_length\"] = 8192"
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]
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},
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{
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"cell_type": "markdown",
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"id": "xa3bpMDtTdBs",
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@ -2338,16 +2414,16 @@
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"outputs": [],
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"source": [
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"LLAMA31_CONFIG_8B = {\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 8192, # Context length\n",
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" \"emb_dim\": 4096, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 32, # Number of layers\n",
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" \"hidden_dim\": 14_336, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # RoPE frequency scaling\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 131_072, # NEW: Larger supported context length\n",
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" \"emb_dim\": 4096, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 32, # Number of layers\n",
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" \"hidden_dim\": 14_336, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # NEW: RoPE frequency scaling\n",
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" \"factor\": 8.0,\n",
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" \"low_freq_factor\": 1.0,\n",
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" \"high_freq_factor\": 4.0,\n",
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@ -2357,17 +2433,17 @@
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"\n",
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"\n",
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"LLAMA32_CONFIG_1B = {\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 8192, # Context length\n",
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" \"emb_dim\": 2048, # NEW: Half the embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 16, # NEW: Half the number of layers\n",
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" \"hidden_dim\": 8192, # NEW: Almost half the size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # RoPE frequency scaling\n",
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" \"factor\": 32.0, # NEW: Adjustment of the rescaling factor\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 131_072, # Context length\n",
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" \"emb_dim\": 2048, # NEW: Half the embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 16, # NEW: Half the number of layers\n",
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" \"hidden_dim\": 8192, # NEW: Almost half the size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # RoPE frequency scaling\n",
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" \"factor\": 32.0, # NEW: Adjustment of the rescaling factor\n",
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" \"low_freq_factor\": 1.0,\n",
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" \"high_freq_factor\": 4.0,\n",
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" \"original_context_length\": 8192,\n",
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@ -2375,6 +2451,24 @@
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5cd351b-d883-460d-9cdc-47e15ddb884a",
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"metadata": {},
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"source": [
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"- Reduce the context length so the model would work fine on a MacBook Air (if you have more RAM, feel free to comment out the lines below):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "387456c3-c6a1-46fe-8830-6e00eb46ac13",
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"metadata": {},
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"outputs": [],
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"source": [
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"LLAMA32_CONFIG[\"context_length\"] = 8192"
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]
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},
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{
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"cell_type": "markdown",
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"id": "Dl4_0EoJKKYv",
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@ -2593,7 +2687,7 @@
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"provenance": []
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},
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"kernelspec": {
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"display_name": "base",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -2607,7 +2701,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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"version": "3.11.4"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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@ -69,9 +69,9 @@
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"output_type": "stream",
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"text": [
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"blobfile version: 3.0.0\n",
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"huggingface_hub version: 0.25.0\n",
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"huggingface_hub version: 0.25.1\n",
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"tiktoken version: 0.7.0\n",
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"torch version: 2.5.0.dev20240812+cu121\n"
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"torch version: 2.4.0\n"
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]
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}
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],
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@ -201,6 +201,25 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"class SharedBuffers:\n",
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" _buffers = {}\n",
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"\n",
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" @staticmethod\n",
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" def get_buffers(context_length, head_dim, rope_base, freq_config, dtype=torch.float32):\n",
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" key = (context_length, head_dim, rope_base, tuple(freq_config.values()) if freq_config else freq_config, dtype)\n",
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"\n",
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" if key not in SharedBuffers._buffers:\n",
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" # Create or fetch the buffers\n",
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" mask = torch.triu(torch.ones(context_length, context_length), diagonal=1)\n",
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" cos, sin = precompute_rope_params(head_dim, rope_base, context_length, freq_config)\n",
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" if dtype is not None:\n",
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" cos = cos.to(dtype)\n",
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" sin = sin.to(dtype)\n",
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" SharedBuffers._buffers[key] = (mask, cos, sin)\n",
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"\n",
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" return SharedBuffers._buffers[key]\n",
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"\n",
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"\n",
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"class GroupedQueryAttention(nn.Module):\n",
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" def __init__(\n",
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" self, d_in, d_out, context_length, num_heads,\n",
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@ -225,13 +244,10 @@
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" self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)\n",
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" self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)\n",
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"\n",
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" self.register_buffer(\"mask\", torch.triu(torch.ones(context_length, context_length), diagonal=1))\n",
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" cos, sin = precompute_rope_params(\n",
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" head_dim=self.head_dim,\n",
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" theta_base=rope_base,\n",
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" freq_config=rope_config,\n",
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" context_length=8192\n",
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" )\n",
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" # Fetch buffers using SharedBuffers\n",
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" mask, cos, sin = SharedBuffers.get_buffers(context_length, self.head_dim, rope_base, rope_config, dtype)\n",
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" self.register_buffer(\"mask\", mask)\n",
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"\n",
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" self.register_buffer(\"cos\", cos)\n",
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" self.register_buffer(\"sin\", sin)\n",
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"\n",
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@ -384,16 +400,16 @@
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"# Llama 3.2 1B\n",
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"\n",
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"LLAMA32_CONFIG = {\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 8192, # Context length\n",
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" \"emb_dim\": 2048, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 16, # Number of layers\n",
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" \"hidden_dim\": 8192, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # RoPE frequency scaling\n",
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" \"vocab_size\": 128_256, # Vocabulary size\n",
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" \"context_length\": 131_072, # Context length\n",
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" \"emb_dim\": 2048, # Embedding dimension\n",
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" \"n_heads\": 32, # Number of attention heads\n",
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" \"n_layers\": 16, # Number of layers\n",
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" \"hidden_dim\": 8192, # Size of the intermediate dimension in FeedForward\n",
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" \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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" \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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" \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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" \"rope_freq\": { # RoPE frequency scaling\n",
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" \"factor\": 32.0,\n",
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" \"low_freq_factor\": 1.0,\n",
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" \"high_freq_factor\": 4.0,\n",
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@ -404,16 +420,16 @@
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"# Llama 3.2 3B\n",
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"\n",
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"# LLAMA32_CONFIG = {\n",
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"# \"vocab_size\": 128_256, # Vocabulary size\n",
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"# \"context_length\": 8192, # Context length\n",
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"# \"emb_dim\": 3072, # Embedding dimension\n",
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"# \"n_heads\": 24, # Number of attention heads\n",
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"# \"n_layers\": 28, # Number of layers\n",
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"# \"hidden_dim\": 8192, # Size of the intermediate dimension in FeedForward\n",
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"# \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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"# \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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"# \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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"# \"rope_freq\": { # RoPE frequency scaling\n",
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"# \"vocab_size\": 128_256, # Vocabulary size\n",
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"# \"context_length\": 131_000, # Context length\n",
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"# \"emb_dim\": 3072, # Embedding dimension\n",
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"# \"n_heads\": 24, # Number of attention heads\n",
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"# \"n_layers\": 28, # Number of layers\n",
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"# \"hidden_dim\": 8192, # Size of the intermediate dimension in FeedForward\n",
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"# \"n_kv_groups\": 8, # Key-Value groups for grouped-query attention\n",
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"# \"rope_base\": 50_000, # The base in RoPE's \"theta\"\n",
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"# \"dtype\": torch.bfloat16, # Lower-precision dtype to save memory\n",
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"# \"rope_freq\": { # RoPE frequency scaling\n",
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"# \"factor\": 32.0,\n",
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"# \"low_freq_factor\": 1.0,\n",
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"# \"high_freq_factor\": 4.0,\n",
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@ -424,9 +440,27 @@
|
||||
"LLAMA_SIZE_STR = \"1B\" if LLAMA32_CONFIG[\"emb_dim\"] == 2048 else \"3B\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "34535172-797e-4dd0-84fb-65bc75ad5b06",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"- Reduce the context length so the model would work fine on a MacBook Air (if you have more RAM, feel free to comment out the lines below):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "a8bc2370-39d2-4bfe-b4c1-6bdd75fe101c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"LLAMA32_CONFIG[\"context_length\"] = 8192"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "156253fe-aacd-4da2-8f13-705f05c4b11e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -434,9 +468,40 @@
|
||||
"model = Llama3Model(LLAMA32_CONFIG)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "19de6c2c-83ce-456d-8be9-6ec415fe9eb1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"- The following is expected to print True to confirm buffers are reused instead of being (wastefully) recreated:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"id": "0e95db6d-2712-41a5-a5e0-86c49897f4cf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"True\n",
|
||||
"True\n",
|
||||
"True\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check buffers\n",
|
||||
"print(model.trf_blocks[0].att.mask is model.trf_blocks[-1].att.mask)\n",
|
||||
"print(model.trf_blocks[0].att.cos is model.trf_blocks[-1].att.cos)\n",
|
||||
"print(model.trf_blocks[0].att.sin is model.trf_blocks[-1].att.sin) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "364e76ca-52f8-4fa5-af37-c4069f9694bc",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -461,7 +526,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 13,
|
||||
"id": "fd5efb03-5a07-46e8-8607-93ed47549d2b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -469,8 +534,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"float32 (PyTorch default): 15.23 GB\n",
|
||||
"bfloat16: 7.61 GB\n"
|
||||
"float32 (PyTorch default): 11.42 GB\n",
|
||||
"bfloat16: 5.71 GB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -505,7 +570,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 14,
|
||||
"id": "31f12baf-f79b-499f-85c0-51328a6a20f5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -531,7 +596,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 15,
|
||||
"id": "9482b01c-49f9-48e4-ab2c-4a4c75240e77",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -631,14 +696,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 16,
|
||||
"id": "e9d96dc8-603a-4cb5-8c3e-4d2ca56862ed",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "fede18d637d24f79a27220fb83bc6d2b",
|
||||
"model_id": "8cdf801700d64fe9b2b827172a8eebcf",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
@ -658,7 +723,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 17,
|
||||
"id": "986bc1a0-804f-4154-80f8-44cefbee1368",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -674,7 +739,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 18,
|
||||
"id": "f5a3014f-4c66-4fe2-874e-7b57562c49ad",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -694,7 +759,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 19,
|
||||
"id": "75166128-5899-4995-9b88-9672e135650e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -775,7 +840,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 20,
|
||||
"id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -818,7 +883,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 21,
|
||||
"id": "7f9f7ccc-70cb-41ff-9c25-44336042fc37",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -845,7 +910,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 22,
|
||||
"id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -902,7 +967,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 23,
|
||||
"id": "1c7a04fa-6aac-416b-8f63-f1e19227633d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -915,10 +980,10 @@
|
||||
"\n",
|
||||
"1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and short grasses.\n",
|
||||
"2. Hay: Llamas also eat hay, which is a dry, compressed form of grass or other plants.\n",
|
||||
"3. Alfalfa: Alfalfa is a legume that is commonly used as a hay substitute in llama feed.\n",
|
||||
"4. Other plants: Llamas will also eat other plants, such as clover, oats, and barley.\n",
|
||||
"3. Alfalfa: Alfalfa is a legume that is commonly fed to llamas, as it is high in protein and fiber.\n",
|
||||
"4. Other plants: Llamas will also eat other plants, such as clover, wild grasses, and shrubs.\n",
|
||||
"\n",
|
||||
"It's worth noting that llamas are adapted to high-altitude environments and can survive on low-quality hay and\n"
|
||||
"It's worth noting that llamas are adapted to high altitudes and\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -982,7 +1047,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -996,7 +1061,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user