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Model: aiplanet/buddhi-128k-chat-7b Source: Original Platform
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README.md
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---
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license: apache-2.0
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pipeline_tag: text-generation
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datasets:
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- aiplanet/buddhi-dataset
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language:
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- en
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---
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<p align="center" style="font-size:34px;"><b>Buddhi-128K-Chat</b></p>
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# Buddhi-128K-Chat (7B) vLLM Inference: [](https://colab.research.google.com/drive/11_8W8FpKK-856QdRVJLyzbu9g-DMxNfg?usp=sharing)
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# Read release article: [🔗 Introducing Buddhi: Open-Source Chat Model with a 128K Context Window 🔗 ](https://medium.aiplanet.com/introducing-buddhi-open-source-chat-model-with-a-128k-context-window-06a1848121d0)
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## Model Description
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Buddhi-128k-Chat is a general-purpose first chat model with 128K context length window. It is meticulously fine-tuned on the Mistral 7B Instruct, and optimised to handle an extended context length of up to 128,000 tokens using the innovative YaRN (Yet another Rope Extension) Technique. This enhancement allows Buddhi to maintain a deeper understanding of context in long documents or conversations, making it particularly adept at tasks requiring extensive context retention, such as comprehensive document summarization, detailed narrative generation, and intricate question-answering.
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## Architecture
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The Buddhi-128K-Chat model is fine-tuned on the Mistral-7B Instruct base model. We selected the Mistral 7B Instruct v0.2 as the parent model due to its superior reasoning capabilities. The architecture of the Mistral-7B model includes features like Grouped-Query Attention and Byte-fallback BPE tokenizer. Originally, this model has 32,768 maximum position embeddings. To increase the context size to 128K, we needed to modify the positional embeddings, which is where YaRN comes into play.
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In our approach, we utilized the NTK-aware technique, which recommends alternative interpolation techniques for positional interpolation. One experimentation involved Dynamic-YARN, suggesting the dynamic value of the 's' scale factor. This is because during inference, the sequence length changes by 1 after every word prediction. By integrating these position embeddings with the Mistral-7B Instruct base model, we achieved the 128K model.
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Additionally, we fine-tuned the model on our dataset to contribute one of the very few 128K chat-based models available in the open-source community with greater reasoning capabilities than all of it.
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### Hardware requirements:
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> For 128k Context Length
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> - 80GB VRAM - A100 Preferred
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> For 32k Context Length
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> - 40GB VRAM - A100 Preferred
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### vLLM - For Faster Inference
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#### Installation
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```
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!pip install vllm
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!pip install flash_attn # If Flash Attention 2 is supported by your System
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```
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Please check out [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) Github Repository for more instructions on how to Install it.
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**Implementation**:
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> Note: The actual hardware requirements to run the model is roughly around 70GB VRAM. For experimentation, we are limiting the context length to 75K instead of 128K. This make it suitable for testing the model in 30-35 GB VRAM
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(
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model='aiplanet/buddhi-128k-chat-7b',
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trust_remote_code=True,
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dtype = 'bfloat16',
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gpu_memory_utilization=1,
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max_model_len= 75000
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)
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prompts = [
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"""<s> [INST] Please tell me a joke. [/INST] """,
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"""<s> [INST] What is Machine Learning? [/INST] """
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]
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=1000
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)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(generated_text)
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print("\n\n")
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# we have also attached a colab notebook, that contains: 2 more experimentations: Long Essay and Entire Book
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```
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For Output, do check out the colab notebook: [](https://colab.research.google.com/drive/11_8W8FpKK-856QdRVJLyzbu9g-DMxNfg?usp=sharing)
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### Transformers - Basic Implementation
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```python
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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model_name = "aiplanet/Buddhi-128K-Chat"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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quantization_config=bnb_config,
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device_map="sequential",
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model,
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trust_remote_code=True
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)
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prompt = "<s> [INST] Please tell me a small joke. [/INST] "
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tokens = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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**tokens,
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max_new_tokens=100,
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do_sample=True,
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top_p=0.95,
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temperature=0.8,
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)
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decoded_output = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
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print(f"Output:\n{decoded_output[len(prompt):]}")
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```
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Output
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```
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Output:
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Why don't scientists trust atoms?
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Because they make up everything.
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```
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## Prompt Template for Buddi-128-Chat
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In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
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```
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"<s>[INST] What is your favourite condiment? [/INST]"
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"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
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"[INST] Do you have mayonnaise recipes? [/INST]"
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```
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# Benchmarks
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### Long Context Benchmark
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<strong>LongICLBench Banking77</strong>
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<div>
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| Model | 1R/2k | 2R/4K | 3R/7K | 4R/9K | 5R/14K |
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|-----------------------------------------|-------|-------|-------|-------|--------|
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| aiplanet/buddhi-128k-chat-7b | 47.8 | 60.8 | 57.8 | 62.4 | 57.2 |
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| NousResearch/Yarn-Mistral-7b-128k | 31.6 | 68.6 | 68 | 47 | 65.6 |
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| CallComply/zephyr-7b-beta-128k | 40.2 | 41.2 | 33.6 | 03 | 0 |
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| Eric111/Yarn-Mistral-7b-128k-DPO | 28.6 | 62.8 | 58 | 41.6 | 59.8 |
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</div>
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<strong>Short Context Benchmark</strong>
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<div>
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| Model | # Params | Average | ARC (25-shot) | HellaSwag (10-shot) | Winogrande (5-shot) | TruthfulOA (0-shot) | MMLU (5-shot) |
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|-----------------------------------|----------|---------|---------------|---------------------|---------------------|---------------------|---------------|
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| aiplanet/buddhi-128k-chat-7b | 7B | 64.42 | 60.84 | 84 | 77.27 | 65.72 | 60.42 |
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| migtissera/Tess-XS-vl-3-yarn-128K | 7B | 62.66 | 61.09 | 82.95 | 74.43 | 50.13 | 62.15 |
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| migtissera/Tess-XS-v1-3-yarn-128K | 7B | 62.49 | 61.6 | 82.96 | 74.74 | 50.2 | 62.1 |
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| Eric111/Yarn-Mistral-7b-128k-DPO | 7B | 60.15 | 60.84 | 82.99 | 78.3 | 43.55 | 63.09 |
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| NousResearch/Yam-Mistral-7b-128k | 7B | 59.42 | 59.64 | 82.5 | 76.95 | 41.78 | 63.02 |
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| CallComply/openchat-3.5-0106-128k | 7B | 59.38 | 64.25 | 77.31 | 77.66 | 46.5 | 57.58 |
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| CallComply/zephyr-7b-beta-128k | 7B | 54.45 | 58.28 | 81 | 74.74 | 46.1 | 53.57 |
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</div>
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## Get in Touch
|
||||
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||||
You can schedule a 1:1 meeting with our DevRel & Community Team to get started with AI Planet Open Source LLMs and GenAI Stack. Schedule the call here: [https://calendly.com/jaintarun](https://calendly.com/jaintarun)
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Stay tuned for more updates and be a part of the coding evolution. Join us on this exciting journey as we make AI accessible to all at AI Planet!
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### Framework versions
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- Transformers 4.39.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Accelerate 0.27.2
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- flash_attn 2.5.6
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### Citation
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```
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@misc {Chaitanya890, lucifertrj ,
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author = { Chaitanya Singhal, Tarun Jain },
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title = { Buddhi-128k-Chat by AI Planet},
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year = 2024,
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url = { https://huggingface.co/aiplanet//Buddhi-128K-Chat },
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publisher = { Hugging Face }
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}
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```
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config.json
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config.json
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{
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"_name_or_path": "aiplanet/buddhi-128k-chat-7b",
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"architectures": [
|
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_mistral.MistralConfig",
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"AutoModelForCausalLM": "modeling_mistral_yarn.MistralForCausalLM"
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},
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"bos_token_id": 1,
|
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 2,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 4.0,
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"finetuned": true,
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"original_max_position_embeddings": 32768,
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"type": "yarn"
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},
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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configuration_mistral.py
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configuration_mistral.py
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# coding=utf-8
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# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Mistral model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
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"mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
|
||||
}
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||||
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|
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class MistralConfig(PretrainedConfig):
|
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r"""
|
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This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
|
||||
Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
||||
with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
|
||||
|
||||
[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
|
||||
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32000):
|
||||
Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`MistralModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 14336):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 8):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
|
||||
The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
|
||||
allows sequence of up to 4096*32 tokens.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
pad_token_id (`int`, *optional*):
|
||||
The id of the padding token.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
The id of the "beginning-of-sequence" token.
|
||||
eos_token_id (`int`, *optional*, defaults to 2):
|
||||
The id of the "end-of-sequence" token.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether the model's input and output word embeddings should be tied.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
|
||||
strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
|
||||
is `{"type": strategy name, "factor": scaling factor}`.
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
sliding_window (`int`, *optional*, defaults to 4096):
|
||||
Sliding window attention window size. If not specified, will default to `4096`.
|
||||
|
||||
|
||||
```python
|
||||
>>> from transformers import MistralModel, MistralConfig
|
||||
|
||||
>>> # Initializing a Mistral 7B style configuration
|
||||
>>> configuration = MistralConfig()
|
||||
|
||||
>>> # Initializing a model from the Mistral 7B style configuration
|
||||
>>> model = MistralModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "mistral"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32000,
|
||||
hidden_size=4096,
|
||||
intermediate_size=14336,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=8,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=4096 * 32,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-6,
|
||||
use_cache=True,
|
||||
pad_token_id=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
tie_word_embeddings=False,
|
||||
rope_scaling={
|
||||
"factor": 16.0,
|
||||
"finetuned": True,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"type": "dynamic-yarn"},
|
||||
rope_theta=10000.0,
|
||||
sliding_window=4096,
|
||||
attention_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_scaling = rope_scaling
|
||||
self.attention_dropout = attention_dropout
|
||||
self.rope_theta = rope_theta
|
||||
self._rope_scaling_validation()
|
||||
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _rope_scaling_validation(self):
|
||||
"""
|
||||
Validate the `rope_scaling` configuration.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
if not isinstance(self.rope_scaling, dict):
|
||||
raise ValueError(
|
||||
"`rope_scaling` must be a dictionary, "
|
||||
f"got {self.rope_scaling}"
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
||||
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "yarn", "dynamic-yarn"]:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'yarn', 'dynamic-yarn'], got {rope_scaling_type}"
|
||||
)
|
||||
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
|
||||
if rope_scaling_type == "yarn" or rope_scaling_type == "dynamic-yarn":
|
||||
original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
|
||||
if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
|
||||
raise ValueError(f"`rope_scaling.original_max_position_embeddings` must be set to an int when using yarn, and dynamic-yarn")
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.39.2"
|
||||
}
|
||||
3
model-00001-of-00003.safetensors
Normal file
3
model-00001-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:2a943387922f9aaed6acbb5a65f5faa1b9b6699d6024fc4a30a500558b17f9d1
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size 4943162336
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3
model-00002-of-00003.safetensors
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3
model-00002-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:d954d2cfd147440423ae9450874a6727e70c7d2a43c094b0542860a0a55934e8
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size 4999819336
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3
model-00003-of-00003.safetensors
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3
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:9750f2739f3487b7d8b0e35b9b51e87a2181f7168eec2144c4c46afcffe64e9f
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size 4540516344
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298
model.safetensors.index.json
Normal file
298
model.safetensors.index.json
Normal file
@@ -0,0 +1,298 @@
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{
|
||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
1638
modeling_mistral_yarn.py
Normal file
1638
modeling_mistral_yarn.py
Normal file
File diff suppressed because it is too large
Load Diff
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"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
|
||||
},
|
||||
"pad_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
|
||||
}
|
||||
}
|
||||
91122
tokenizer.json
Normal file
91122
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
44
tokenizer_config.json
Normal file
44
tokenizer_config.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "</s>",
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user