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Add Mixtral-8x22B #1845

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Dec 2, 2024
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Add Mixtral-8x22B #1845

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8a9b0ba
Add Mixtral-8x22B
ysjprojects Nov 30, 2024
9dc5d45
Update README.md
ysjprojects Nov 30, 2024
b94bf90
Update README.md
ysjprojects Nov 30, 2024
319f462
Mixtral-8x22B fix: added n_embd
ysjprojects Nov 30, 2024
191aa34
Mixtral-8x22B minor vocab_size fix
ysjprojects Nov 30, 2024
b044fad
Merge branch 'main' into mixtral8x22b
Andrei-Aksionov Dec 2, 2024
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3 changes: 2 additions & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ Every model is written from scratch to maximize performance and remove layers of
|----|----|----|----|
| Llama 3, 3.1, 3.2 | 1B, 3B, 8B, 70B, 405B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama3) |
| Code Llama | 7B, 13B, 34B, 70B | Meta AI | [Rozière et al. 2023](https://arxiv.org/abs/2308.12950) |
| Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/) |
| Mixtral MoE | 8x7B, 8x22B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/) |
| Mistral | 7B, 123B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/) |
| CodeGemma | 7B | Google | [Google Team, Google Deepmind](https://ai.google.dev/gemma/docs/codegemma) |
| Gemma 2 | 2B, 9B, 27B | Google | [Google Team, Google Deepmind](https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf) |
Expand Down Expand Up @@ -128,6 +128,7 @@ Every model is written from scratch to maximize performance and remove layers of
| MicroLlama | 300M | Ken Wang | [MicroLlama repo](https://github.com/keeeeenw/MicroLlama) |
| Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/) |
| Mistral | 7B, 123B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/) |
| Mixtral MoE | 8x22B | Mistral AI | [Mistral AI 2024](https://mistral.ai/news/mixtral-8x22b/) |
| OLMo | 1B, 7B | Allen Institute for AI (AI2) | [Groeneveld et al. 2024](https://aclanthology.org/2024.acl-long.841/) |
| OpenLLaMA | 3B, 7B, 13B | OpenLM Research | [Geng & Liu 2023](https://github.com/openlm-research/open_llama) |
| Phi 1.5 & 2 | 1.3B, 2.7B | Microsoft Research | [Li et al. 2023](https://arxiv.org/abs/2309.05463) |
Expand Down
21 changes: 21 additions & 0 deletions litgpt/config.py
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Expand Up @@ -1520,6 +1520,27 @@ def norm_class(self) -> Type:
n_expert=8,
n_expert_per_token=2,
),
# https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1/blob/main/config.json
dict(
name="Mixtral-8x22B-{}v0.1",
hf_config=dict(org="mistralai", name="Mixtral-8x22B-{}v0.1"),
padded_vocab_size=32000,
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block_size=65536,
n_layer=56,
n_query_groups=8,
rotary_percentage=1.0,
parallel_residual=False,
bias=False,
norm_class_name="RMSNorm",
norm_eps=1e-05,
mlp_class_name="LLaMAMoE",
intermediate_size=16384,
n_head=48,
n_embd=6144,
rope_base=1000000,
n_expert=8,
n_expert_per_token=2,
),
]
for c in mistral:
for kind in ("", "Instruct-"):
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5 changes: 3 additions & 2 deletions tests/test_convert_lit_checkpoint.py
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Expand Up @@ -156,9 +156,10 @@ def test_against_hf_llama2(ours_kwargs):


@torch.inference_mode()
def test_against_mixtral():
@pytest.mark.parametrize("model_name", ("Mixtral-8x7B-Instruct-v0.1", "Mixtral-8x22B-Instruct-v0.1"))
def test_against_mixtral(model_name):
ours_config = Config.from_name(
"Mixtral-8x7B-Instruct-v0.1",
model_name,
padded_vocab_size=10000,
n_layer=2,
n_embd=32,
Expand Down
5 changes: 3 additions & 2 deletions tests/test_model.py
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Expand Up @@ -512,11 +512,12 @@ def test_against_mathstral_hf_models(device, dtype):


@torch.inference_mode()
def test_against_hf_mixtral():
@pytest.mark.parametrize("model_name", ("Mixtral-8x7B-Instruct-v0.1", "Mixtral-8x22B-Instruct-v0.1"))
def test_against_hf_mixtral(model_name):
device = torch.device("cpu")
dtype = torch.float32
ours_config = Config.from_name(
"Mixtral-8x7B-Instruct-v0.1",
model_name,
padded_vocab_size=10000,
n_layer=2,
n_embd=32,
Expand Down
3 changes: 3 additions & 0 deletions tutorials/download_model_weights.md
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Expand Up @@ -26,6 +26,7 @@ LitGPT supports a variety of LLM architectures with publicly available weights.
| MicroLlama | 300M | Ken Wang | [MicroLlama repo](https://github.com/keeeeenw/MicroLlama)
| Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/) |
| Mistral | 7B, 123B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/) |
| Mixtral MoE | 8x22B | Mistral AI | [Mistral AI 2024](https://mistral.ai/news/mixtral-8x22b/) |
| Nous-Hermes | 7B, 13B, 70B | NousResearch | [Org page](https://huggingface.co/NousResearch) |
| OLMo | 1B, 7B | Allen Institute for AI (AI2) | [Groeneveld et al. 2024](https://aclanthology.org/2024.acl-long.841/) |
| OpenLLaMA | 3B, 7B, 13B | OpenLM Research | [Geng & Liu 2023](https://github.com/openlm-research/open_llama) |
Expand Down Expand Up @@ -156,6 +157,8 @@ mistralai/Mistral-7B-v0.3
mistralai/Mistral-Large-Instruct-2407
mistralai/Mixtral-8x7B-Instruct-v0.1
mistralai/Mixtral-8x7B-v0.1
mistralai/Mixtral-8x22B-Instruct-v0.1
mistralai/Mixtral-8x22B-v0.1
NousResearch/Nous-Hermes-13b
NousResearch/Nous-Hermes-llama-2-7b
NousResearch/Nous-Hermes-Llama2-13b
Expand Down
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