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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1a848dfb-4083-4d7c-af83-82e663d1f964",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer\n",
"\n",
"MODEL_ID = \"/workspace/mixtral-reasoning-output/checkpoint-275/\"\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_ID,\n",
" device_map=\"auto\",\n",
" torch_dtype=torch.bfloat16,\n",
" attn_implementation=\"flash_attention_2\",\n",
" trust_remote_code=True\n",
" )\n",
" \n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "419c3212-c843-4102-850c-ec2e83e5401a",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "002d4810-e64d-4553-9256-d5a95bad07da",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"detailed thinking on\"\n",
"user_prompt = \"\"\"Triangle $ABC$ has a right angle at $B$. Points $D$ and $E$ are chosen on $\\overline{AC}$ and $\\overline{BC}$, respectively, such that $AB = BE = ED = DC = 2$. Find the area of $\\triangle CDE$.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6ba5056-67ea-40d7-a6bb-fdb2d750cfc7",
"metadata": {},
"outputs": [],
"source": [
"# Fix the pad token issue\n",
"if tokenizer.pad_token is None or tokenizer.pad_token_id == tokenizer.eos_token_id:\n",
" tokenizer.pad_token = tokenizer.unk_token\n",
" tokenizer.pad_token_id = tokenizer.unk_token_id\n",
"\n",
"# Verify the fix\n",
"print(f\"EOS token ID: {tokenizer.eos_token_id}\")\n",
"print(f\"PAD token ID: {tokenizer.pad_token_id}\")\n",
"print(f\"UNK token ID: {tokenizer.unk_token_id}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3af2af88-aa27-4670-a78b-bea00bc07414",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"]\n",
"\n",
"# Tokenize input\n",
"input_ids = tokenizer.apply_chat_template(\n",
" messages,\n",
" tokenize=True,\n",
" add_generation_prompt=True,\n",
" return_tensors=\"pt\"\n",
").to(\"cuda\")\n",
"\n",
"# Create streamer - TextStreamer automatically prints to stdout\n",
"streamer = TextStreamer(\n",
" tokenizer, \n",
" skip_special_tokens=False,\n",
" skip_prompt=False,\n",
")\n",
"\n",
"# Generate with streamer - no threading needed with TextStreamer\n",
"model.generate(\n",
" input_ids=input_ids,\n",
" pad_token_id=tokenizer.eos_token_id\n",
" streamer=streamer,\n",
" max_new_tokens=16383,\n",
" temperature=0.5,\n",
" top_p=0.95,\n",
" top_k=40,\n",
" repetition_penalty=1.2,\n",
" do_sample=True,\n",
" #use_cache=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f4daeb6-02c6-4376-acc8-2b34fbb9fbd7",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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