Jamba Hercules

🚀 Jamba-Hercules

Jamba-Hercules是一個文本生成模型，基於特定數據集訓練，能處理文本生成任務，可用於生成富有想象力的文本內容，如示例中樹成為太空生物的過程描述。

🚀 快速開始

模型信息

數據集使用說明

使用的數據集為 Locutusque/hercules-v4.0，使用了前 10k 個示例。

💻 使用示例

基礎用法

# 此代碼會自動將 "<|startoftext|>" 特殊標記添加到任何輸入中。在推理時，必須將此標記添加到所有輸入中，因為初步測試表明，不添加該標記會導致輸出錯誤。
!pip install -qqq transformers>=4.39.0 mamba-ssm causal-conv1d>=1.2.0 accelerate bitsandbytes --progress-bar off
!pip install flash-attn --no-build-isolation

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

double_quant_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "Severian/Jamba-Hercules",
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    quantization_config=double_quant_config,
)
tokenizer = AutoTokenizer.from_pretrained("Severian/Jamba-Hercules")

input_text = """<|im_start|>system 
You are a helpful AI assistant.
<|im_end|> 
<|im_start|>user
A tree needs to leave the earth and become astro-biological. How can it achieve it's goal?
<|im_end|> 
<|im_start|>assistant
"""

input_ids = tokenizer(input_text, return_tensors='pt').to(model.device)["input_ids"]

outputs = model.generate(input_ids, max_new_tokens=1024, temperature=0.0, repetition_penalty=1.1)

print(tokenizer.batch_decode(outputs)[0])
# <|startoftext|><|im_start|>system 
# You are a helpful AI assistant.
# <|im_end|> 
# <|im_start|>user
# A tree needs to leave the earth and become astro-biological. How can it achieve it's goal?
# <|im_end|> 
# <|im_start|>assistant
# The process of becoming astronautic life for a tree is an excemtally complicated one that requires a great deal of preparation, coordination, and funding. The first step in this process is for the tree to be identified as a potential candidate for astronautics, which may involve a thorough assessment of its size, health, and suitability for space travel.
# Once a tree has been identified as a possible astronautic life-form, the next step involves preparing it for spaceflight. This includes training the tree to tolerate low gravity environments, teaching it how to navigate in zero gravity, and ensuring that its nutritional needs are met in space. The process of acclimating a tree to spaceflight can take months or even years, depending on the species and the specific requirements of each mission.
# Once the tree has been adequately trained and prepared for spaceflight, it must then be transported into orbit or beyond. This may involve launching the tree aboard a rocket or shuttle, or using other methods such as balloons or parachutes to reach its destination. The journey itself can be long and arduous, with many potential risks and challenges along the way.
# Upon arrival in space, the tree will need to be monitored closely and cared for by astronautic personnel. This may involve feeding the tree, maintaining its health and well-being, and ensuring that it has enough water and nutrients to survive. The tree may also need to be protected from radiation, extreme temperatures, or other hazards that it may face in space.
# Over time, as the tree adapts to life in space, it will likely undergo some changes in its physical appearance and behavior. For example, it may begin growing in new ways, developing different kinds of leaves or branches in response to the unique environment it finds itself in. It may also become more active and mobile, using its roots to navigate and explore its new home.
# Throughout this process, there are many challenges that need to be addressed and overcome.<|im_end|>
# <|endoftext|>

示例輸出

<|startoftext|><|im_start|>system 
You are a helpful AI assistant.
<|im_end|> 
<|im_start|>user
A tree needs to leave the earth and become astro-biological. How can it achieve it's goal?
<|im_end|> 
<|im_start|>assistant
The process of becoming astronautic life for a tree is an excemtally complicated one that requires a great deal of preparation, coordination, and funding. The first step in this process is for the tree to be identified as a potential candidate for astronautics, which may involve a thorough assessment of its size, health, and suitability for space travel.
Once a tree has been identified as a possible astronautic life-form, the next step involves preparing it for spaceflight. This includes training the tree to tolerate low gravity environments, teaching it how to navigate in zero gravity, and ensuring that its nutritional needs are met in space. The process of acclimating a tree to spaceflight can take months or even years, depending on the species and the specific requirements of each mission.
Once the tree has been adequately trained and prepared for spaceflight, it must then be transported into orbit or beyond. This may involve launching the tree aboard a rocket or shuttle, or using other methods such as balloons or parachutes to reach its destination. The journey itself can be long and arduous, with many potential risks and challenges along the way.
Upon arrival in space, the tree will need to be monitored closely and cared for by astronautic personnel. This may involve feeding the tree, maintaining its health and well-being, and ensuring that it has enough water and nutrients to survive. The tree may also need to be protected from radiation, extreme temperatures, or other hazards that it may face in space.
Over time, as the tree adapts to life in space, it will likely undergo some changes in its physical appearance and behavior. For example, it may begin growing in new ways, developing different kinds of leaves or branches in response to the unique environment it finds itself in. It may also become more active and mobile, using its roots to navigate and explore its new home.
Throughout this process, there are many challenges that need to be addressed and overcome.<|im_end|>
<|endoftext|>

🔧 技術細節

訓練信息

Hercules-v4.0 首次測試

• 1250 步（使用 A100 訓練 5 小時）
• 最終損失：0.98

超參數

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["embed_tokens", "x_proj", "in_proj", "out_proj"],
    lora_dropout=0.05,
    task_type="CAUSAL_LM",
    bias="none"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=train_dataset,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    tokenizer=tokenizer,
    args=TrainingArguments(
        num_train_epochs=1,
        lr_scheduler_type='cosine',
        learning_rate=0.0002,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=8,
        gradient_checkpointing=True,
        warmup_steps=10,
        weight_decay=0.01,
        fp16=not torch.cuda.is_bf16_supported(),
        bf16=torch.cuda.is_bf16_supported(),
        logging_steps=1,
        save_steps=200,
        output_dir="outputs",
        optim="adamw_bnb_8bit",
        adam_epsilon=0.00001,
        adam_beta2=0.95,
        max_grad_norm=1.0,
        seed=42,
    ),
)

📄 許可證

本項目採用 Apache-2.0 許可證。