new

README.md

@@ -1,3 +1,8 @@
 ---
 license: apache-2.0
 ---
+
+This is a tiny random Llama model derived from ["meta-llama/Llama-2-7b-hf"](https://huggingface.co/meta-llama/Llama-2-7b-hf).
+
+See [make_tiny_model.py](./make_tiny_model.py) for how this was done.
+

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-2-7b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 16,
+  "initializer_range": 0.02,
+  "intermediate_size": 64,
+  "max_position_embeddings": 256,
+  "model_type": "llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0.dev0",
+  "use_cache": true,
+  "vocab_size": 3000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.0.dev0"
+}

make_tiny_model.py

@@ -0,0 +1,54 @@
+import subprocess
+import shlex
+import torch
+from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizerFast
+
+
+mname_from = "meta-llama/Llama-2-7b-hf"
+mname_tiny = "tiny-random-llama-2"
+vocab_keep_items = 3000
+
+config = LlamaConfig.from_pretrained(mname_from)
+# print("orig config", config)
+config.update(dict(
+    hidden_size=16,
+    intermediate_size=64,
+    num_attention_heads=4,
+    num_hidden_layers=2,
+    max_position_embeddings=256,
+    num_key_value_heads=4,
+    vocab_size=vocab_keep_items,
+))
+print("new config", config)
+
+# create a tiny random model
+tiny_model = LlamaForCausalLM(config)
+print(f"num of params {tiny_model.num_parameters()}")
+
+# shrink it more and save
+tiny_model.bfloat16() # half-size
+tiny_model.save_pretrained(mname_tiny)
+
+# shrink the tokenizer from 32k to 3k vocab
+tokenizer_fast = LlamaTokenizerFast.from_pretrained(mname_from)
+tmp_dir = f"/tmp/{mname_from}"
+tokenizer_fast.save_pretrained(tmp_dir)
+# resize tokenizer.json (vocab.txt will be automatically resized on save_pretrained)
+# perl  -0777 -pi -e 's|(2999).*|$1},"merges": []}}|msg' tokenizer.json # 0-indexed, so vocab_keep_items-1!
+closing_pat = '},"merges": []}}'
+cmd = (f"perl -0777 -pi -e 's|({vocab_keep_items-1}).*|$1{closing_pat}|msg' {tmp_dir}/tokenizer.json")
+#print(f"Running:\n{cmd}")
+result = subprocess.run(shlex.split(cmd), capture_output=True, text=True)
+#print(result)
+
+# reload with modified tokenizer
+tokenizer_fast_tiny = LlamaTokenizerFast.from_pretrained(tmp_dir)
+tokenizer_fast_tiny.save_pretrained(mname_tiny)
+
+# test the new model and tokenizer function
+model_inputs = tokenizer_fast_tiny("Making tiny model", return_tensors="pt")
+gen_tokens = tiny_model.generate(**model_inputs, max_new_tokens=100)
+print(tokenizer_fast_tiny.batch_decode(gen_tokens, skip_special_tokens=True))
+print("Random output should be expected, but no crashing")
+
+print(f"Model+Tokenizer saved in {mname_tiny}")

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:852db1b39acb2336abc997440c6f6d6e4ab640f91e5e2aa9e2488d5794159d30
+size 210712

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

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+}