Support for new Llama 3.2 - 1B / 3B ?

[David-AU-github]

Hi:

Tried a merge (franken) (pass) of these models and got a error :

3B:

File "F:\mergekit2\mergekit\mergekit\io\tasks.py", line 86, in execute raise RuntimeError( RuntimeError: Tensor lm_head.weight required but not present in model g:/3B/Llama-3.2-3B-Instruct

1B:

File "F:\mergekit2\mergekit\mergekit\io\tasks.py", line 86, in execute raise RuntimeError( RuntimeError: Tensor lm_head.weight required but not present in model g:/1B/Llama-3.2-1B-Instruct

NOTE: Verified both models of these are "gguf-able" ; and work correctly.

Thanks

INDEX file for "3B" (not avail for 1B, as it is single SFT file):

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[appvoid]

it appears they slightly changed the names of some things there, not an expert but will try to see if i can find any differences

[David-AU-github]

it appears they slightly changed the names of some things there, not an expert but will try to see if i can find any differences

Seems the issue may be here: https://github.com/arcee-ai/mergekit/blob/main/mergekit/_data/architectures/llama.json

But this may mess up llama 2,3, 3.1 merges? Or it needs another "alias" added ?

[appvoid]

i tried a little but i'm not going anywhere with this, we will need someone that can actually crack the code here, in the meantime, i leave my findings.

counterintuitive "lm_head.weight not found" error makes it hard to debug as the lm_head is there, maybe we should tweak something at llama.json, maybe adding support for rope, here's a comparison between palmer-003 (tinyllama based that is compatible with passthrough) and llama 3.2 1b.

Feature	palmer-003	Llama 3 1b
Number of layers	22	16
Hidden size	2048	2048
Intermediate size	5632	8192
Attention heads	32	32
Key-value heads	4	8
Max position embeddings	2048	131072
RoPE theta	10000.0	500000.0
RoPE scaling	null	{ "factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3" }
Vocabulary size	32000	128256
BOS token ID	1	128000
EOS token ID	2	128001
Tie word embeddings	false	true
Torch dtype	float16	bfloat16
Head dim	Not specified	64
MLP bias	Not specified	false
Pretraining tp	1	1
Use cache	true	true
Attention bias	false	false
Attention dropout	0.0	0.0
Hidden act	silu	silu
Initializer range	0.02	0.02
RMS norm eps	1e-05	1e-05
Model type	llama	llama
Architectures	["LlamaForCausalLM"]	["LlamaForCausalLM"]

Key differences:

• Llama 3 1b has additional configurations like head_dim and mlp_bias.
• Llama 3 1b uses more advanced positional encoding (RoPE scaling).
• Llama 3 1b ties word embeddings, while palmer-003 doesn't.
• The models use different tokenization schemes and token IDs.
• Llama 3 1b has a larger intermediate size and vocabulary, but fewer layers.

[cyc00518]

Hi, guys

So, is it still not possible to merge different generations of LLaMA models?

Or, taking it a step further, is it possible to merge models with different architectures, like Mistral and LLaMA?

I genuinely hope that this repo can provide more comprehensive explanations for these fundamental aspects, please

[Jacobsolawetz]

That's right @cyc00518 cross arch merge is not supported (yet) - it is a deep area of research for us

[cyc00518]

@Jacobsolawetz thanks! Btw,I am also curious about #301. Have you gotten the answer?

[SicariusSicariiStuff]

will llama 3.2 be supported?

[cg123]

Llama 3.2 is supported as of 852291726650c8dd6ac78721c2c4d0fbdafc8e3d - if it's giving you trouble let me know!

[SicariusSicariiStuff]

it merges fine now, but the model gets error on inference. maybe I messed something up though.

[cg123]

it merges fine now, but the model gets error on inference. maybe I messed something up though.

What error are you getting during inference? And could I get an example of a config that's failing?

[appvoid]

I'm getting this when benchmarking on lm-eval-harness:

Traceback (most recent call last):
  File "/usr/local/bin/lm_eval", line 8, in <module>
    sys.exit(cli_evaluate())
  File "/content/lm-evaluation-harness/lm_eval/__main__.py", line 382, in cli_evaluate
    results = evaluator.simple_evaluate(
  File "/content/lm-evaluation-harness/lm_eval/utils.py", line 397, in _wrapper
    return fn(*args, **kwargs)
  File "/content/lm-evaluation-harness/lm_eval/evaluator.py", line 204, in simple_evaluate
    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
  File "/content/lm-evaluation-harness/lm_eval/api/model.py", line 147, in create_from_arg_string
    return cls(**args, **args2)
  File "/content/lm-evaluation-harness/lm_eval/models/huggingface.py", line 172, in __init__
    self._create_tokenizer(
  File "/content/lm-evaluation-harness/lm_eval/models/huggingface.py", line 683, in _create_tokenizer
    self.tokenizer = transformers.AutoTokenizer.from_pretrained(
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py", line 897, in from_pretrained
    return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py", line 2271, in from_pretrained
    return cls._from_pretrained(
  File "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py", line 2505, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_fast.py", line 115, in __init__
    fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
Exception: data did not match any variant of untagged enum ModelWrapper at line 1251003 column 3

So, I guess I'm not the only having issues...

[cg123]

This should go away if you update transformers and tokenizers.