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stockmark
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stockmark
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Stockmark-2-100B-Instruct is a 100-billion-parameter large language model built from scratch, with a particular focus on Japanese. It was pre-trained on approximately 2.0 trillion tokens of data, consisting of 60% English, 30% Japanese, and 10% code. Following pretraining, the model underwent post-training (SFT and DPO) with synthetic data in Japanese to enhance its ability to follow instructions. This version improves instruction-following ability and adds support for long-context (32k), compared to the previous version (Stockmark-2-100B-Instruct-beta).
This project was supported by GENIAC.
| Model | Average | coding | extraction | humanities | math | reasoning | roleplay | stem |
|---|---|---|---|---|---|---|---|---|
| Stockmark-2-100B-Instruct | 7.87 | 7.07 | 8.35 | 8.73 | 7.57 | 5.45 | 8.65 | 8.33 |
| Stockmark-2-100B-Instruct-beta | 7.71 | 6.73 | 8.23 | 8.63 | 7.01 | 5.85 | 8.54 | 8.07 |
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "stockmark/Stockmark-2-100B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="bfloat16") instruction = "自然言語処理とは？"input_ids = tokenizer.apply_chat_template( [{"role": "user", "content": instruction}], add_generation_prompt=True, return_tensors="pt").to(model.device) with torch.inference_mode(): tokens = model.generate( input_ids, max_new_tokens = 512, do_sample = True, temperature = 0.7, top_p = 0.95 ) output = tokenizer.decode(tokens[0], skip_special_tokens=True)print(output)python
from vllm import LLM, SamplingParams llm = LLM( model="stockmark/Stockmark-2-100B-Instruct", tensor_parallel_size=4, dtype="bfloat16") sampling_params = SamplingParams( temperature=0.7, top_p=0.95, max_tokens=512) conversation = [{"role": "user", "content": "自然言語処理とは？"}] outputs = llm.chat(conversation, sampling_params=sampling_params) for output in outputs: generated_text = output.outputs[0].text print(generated_text)import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "stockmark/Stockmark-2-100B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="bfloat16") instruction = "自然言語処理とは？"input_ids = tokenizer.apply_chat_template( [{"role": "user", "content": instruction}], add_generation_prompt=True, return_tensors="pt").to(model.device) with torch.inference_mode(): tokens = model.generate( input_ids, max_new_tokens = 512, do_sample = True, temperature = 0.7, top_p = 0.95 ) output = tokenizer.decode(tokens[0], skip_special_tokens=True)print(output)from vllm import LLM, SamplingParams llm = LLM( model="stockmark/Stockmark-2-100B-Instruct", tensor_parallel_size=4, dtype="bfloat16") sampling_params = SamplingParams( temperature=0.7, top_p=0.95, max_tokens=512) conversation = [{"role": "user", "content": "自然言語処理とは？"}] outputs = llm.chat(conversation, sampling_params=sampling_params) for output in outputs: generated_text = output.outputs[0].text print(generated_text)