feat: add Llama-3.2-[1B/3B] support

[stillmatic]

This PR adds support for the small models in the Llama 3.2 release - 1B and 3B.

There are two pieces of custom logic we needed to implement for this to work:

1. Support safetensors files without merging. Larger models require multiple safetensors files to fit, but the 1B model fits into a single exported file. As a result, we refactor the HF conversion script logic, and skip the state dict merging if we can find a single file with all the weights.
2. Support tied embedding weights. The smaller models share input/output embeddings, presumably to save parameters, but the larger models don't. In the conversion step, we check if the output weights are empty and if so copy the input embeddings over.

I think this is all we need - the local testing generates reasonable results for 1B, 3B, and 8B (eager and compiled). note: in local testing I needed to comment out torch._functorch.config.enable_autograd_cache = True with torch 2.4.1 - I assume this was merged and supported by default?

Testing:
3B model:

export MODEL_REPO=meta-llama/Llama-3.2-3B-Instruct; python generate.py --max_new_tokens 16 --checkpoint_path "checkpoints/${MODEL_REPO}/model.pth"
 --compile --prompt "Hello, my name is"
Compilation time: 38.25 seconds
<|begin_of_text|>Hello, my name is [Your Name] and I am excited to be participating in the [Event/
Time for inference 1: 0.14 sec total, 116.02 tokens/sec
Bandwidth achieved: 745.49 GB/s
FLOPS achieved: 1.03 TF/s

<|begin_of_text|>Hello, my name is Ryan. I'm a photographer and artist, and I'm thrilled to be a
Time for inference 2: 0.19 sec total, 82.60 tokens/sec
Bandwidth achieved: 530.73 GB/s
FLOPS achieved: 0.73 TF/s

<|begin_of_text|>Hello, my name is Ryan and I'll be guiding you through a series of challenges designed to test your
Time for inference 3: 0.19 sec total, 82.31 tokens/sec
Bandwidth achieved: 528.87 GB/s
FLOPS achieved: 0.73 TF/s

<|begin_of_text|>Hello, my name is Emily, and I'm here to visit the top 11 most amazing aquariums
Time for inference 4: 0.19 sec total, 82.12 tokens/sec
Bandwidth achieved: 527.65 GB/s
FLOPS achieved: 0.73 TF/s

<|begin_of_text|>Hello, my name is (name), and I'm a (your profession). Welcome to our practice!
Time for inference 5: 0.20 sec total, 80.32 tokens/sec
Bandwidth achieved: 516.08 GB/s
FLOPS achieved: 0.71 TF/s

==========
Batch Size: 1
Prompt Length: 6
Generated tokens: 16
Average tokens/sec: 88.67
Memory used: 7.55 GB

1B model:

export MODEL_REPO=meta-llama/Llama-3.2-1B-Instruct; python generate.py --max_new_tokens 16 --checkpoint_path "checkpoints/${MODEL_REPO}/model.pth" --compile --prompt "Hello, my name is"
Compilation time: 25.37 seconds
<|begin_of_text|>Hello, my name is [Your Name] and I am excited to be a part of the [Company
Time for inference 1: 0.06 sec total, 249.87 tokens/sec
Bandwidth achieved: 617.58 GB/s
FLOPS achieved: 0.85 TF/s

<|begin_of_text|>Hello, my name is Bertha.

I am a 16-year-old girl, living in a world
Time for inference 2: 0.06 sec total, 247.61 tokens/sec
Bandwidth achieved: 612.01 GB/s
FLOPS achieved: 0.84 TF/s

<|begin_of_text|>Hello, my name is Svetlana, and I am a 32-year-old yoga teacher from
Time for inference 3: 0.06 sec total, 257.50 tokens/sec
Bandwidth achieved: 636.45 GB/s
FLOPS achieved: 0.88 TF/s

<|begin_of_text|>Hello, my name is Emily and I'm the founder of Green Gloop & Co., a sustainable food
Time for inference 4: 0.06 sec total, 251.88 tokens/sec
Bandwidth achieved: 622.55 GB/s
FLOPS achieved: 0.86 TF/s

<|begin_of_text|>Hello, my name is Emily and I'm a huge fan of your channel. Your content is always so
Time for inference 5: 0.07 sec total, 245.82 tokens/sec
Bandwidth achieved: 607.57 GB/s
FLOPS achieved: 0.84 TF/s

==========
Batch Size: 1
Prompt Length: 6
Generated tokens: 16
Average tokens/sec: 250.54
Memory used: 3.30 GB

8B model (regression check)

export MODEL_REPO=meta-llama/Meta-Llama-3-8B-Instruct; python generate.py --max_new_tokens 16 --checkpoint_path "checkpoints/${MODEL_REPO}/model.pth" --compile --prompt "Hello, my name is"
Compilation time: 10.54 seconds
<|begin_of_text|>Hello, my name is [Your Name] and I am excited to be here. I have been writing
Time for inference 1: 0.30 sec total, 53.07 tokens/sec
Bandwidth achieved: 796.64 GB/s
FLOPS achieved: 1.10 TF/s

<|begin_of_text|>Hello, my name is Ryan and I'm a photographer based in the Pacific Northwest. I specialize in outdoor
Time for inference 2: 0.30 sec total, 53.73 tokens/sec
Bandwidth achieved: 806.46 GB/s
FLOPS achieved: 1.11 TF/s

<|begin_of_text|>Hello, my name is Svetlana, and I am a certified yoga instructor. I am passionate
Time for inference 3: 0.30 sec total, 53.24 tokens/sec
Bandwidth achieved: 799.12 GB/s
FLOPS achieved: 1.10 TF/s

<|begin_of_text|>Hello, my name is Emily and I'm the founder of Art for Recovery. Art for Recovery is a
Time for inference 4: 0.30 sec total, 52.53 tokens/sec
Bandwidth achieved: 788.53 GB/s
FLOPS achieved: 1.08 TF/s

<|begin_of_text|>Hello, my name is Emily and I'm a yoga instructor and a Reiki Master. I'm here
Time for inference 5: 0.31 sec total, 52.29 tokens/sec
Bandwidth achieved: 784.94 GB/s
FLOPS achieved: 1.08 TF/s

==========
Batch Size: 1
Prompt Length: 6
Generated tokens: 16
Average tokens/sec: 52.97
Memory used: 16.35 GB

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