Intel_-_neural-chat-7b-v3-3-gguf

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neural-chat-7b-v3-3 - GGUF

• Model creator: https://huggingface.co/Intel/
• Original model: https://huggingface.co/Intel/neural-chat-7b-v3-3/

Original model description:

license: apache-2.0 tags:

• LLMs
• mistral
• math
• Intel base_model: Intel/neural-chat-7b-v3-1 model-index:
• name: neural-chat-7b-v3-3 results:
  • task: type: Large Language Model name: Large Language Model dataset: name: meta-math/MetaMathQA type: meta-math/MetaMathQA metrics:
    • type: ARC (25-shot) value: 66.89 name: ARC (25-shot) verified: true
    • type: HellaSwag (10-shot) value: 85.26 name: HellaSwag (10-shot) verified: true
    • type: MMLU (5-shot) value: 63.07 name: MMLU (5-shot) verified: true
    • type: TruthfulQA (0-shot) value: 63.01 name: TruthfulQA (0-shot) verified: true
    • type: Winogrande (5-shot) value: 79.64 name: Winogrande (5-shot) verified: true
    • type: GSM8K (5-shot) value: 61.11 name: GSM8K (5-shot) verified: true
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 66.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 85.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 63.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 63.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 79.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 61.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard

---

Model Details: Neural-Chat-v3-3

This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from the Intel/neural-chat-7b-v3-1 on the meta-math/MetaMathQA dataset. The model was aligned using the Direct Performance Optimization (DPO) method with Intel/orca_dpo_pairs. The Intel/neural-chat-7b-v3-1 was originally fine-tuned from mistralai/Mistral-7B-v-0.1. For more information, refer to the blog The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2.

<p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6297f0e30bd2f58c647abb1d/ctASHUT5QYIxMsOFa-sHC.webp" width="500"/> Photo by Google DeepMind on Unsplash </p>

How To Use

Context length for this model: 8192 tokens (same as https://huggingface.co/mistralai/Mistral-7B-v0.1)

Reproduce the model

Here is the sample code to reproduce the model: GitHub sample code. Here is the documentation to reproduce building the model:

git clone https://github.com/intel/intel-extension-for-transformers.git
cd intel-extension-for-transformers

docker build --no-cache ./ --target hpu --build-arg REPO=https://github.com/intel/intel-extension-for-transformers.git --build-arg ITREX_VER=main -f ./intel_extension_for_transformers/neural_chat/docker/Dockerfile -t chatbot_finetuning:latest

docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host chatbot_finetuning:latest

# after entering docker container
cd examples/finetuning/finetune_neuralchat_v3

We select the latest pretrained mistralai/Mistral-7B-v0.1 and the open source dataset Open-Orca/SlimOrca to conduct the experiment.

The below script use deepspeed zero2 to lanuch the training with 8 cards Gaudi2. In the finetune_neuralchat_v3.py, the default use_habana=True, use_lazy_mode=True, device="hpu" for Gaudi2. And if you want to run it on NVIDIA GPU, you can set them use_habana=False, use_lazy_mode=False, device="auto".

deepspeed --include localhost:0,1,2,3,4,5,6,7 \
    --master_port 29501 \
    finetune_neuralchat_v3.py

Merge the LoRA weights:

python apply_lora.py \
    --base-model-path mistralai/Mistral-7B-v0.1 \
    --lora-model-path finetuned_model/ \
    --output-path finetuned_model_lora

Use the model

FP32 Inference with Transformers

import transformers


model_name = 'Intel/neural-chat-7b-v3-3'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

def generate_response(system_input, user_input):

    # Format the input using the provided template
    prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"

    # Tokenize and encode the prompt
    inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)

    # Generate a response
    outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Extract only the assistant's response
    return response.split("### Assistant:\n")[-1]


# Example usage
system_input = "You are a math expert assistant. Your mission is to help users understand and solve various math problems. You should provide step-by-step solutions, explain reasonings and give the correct answer."
user_input = "calculate 100 + 520 + 60"
response = generate_response(system_input, user_input)
print(response)

# expected response
"""
To calculate the sum of 100, 520, and 60, we will follow these steps:

1. Add the first two numbers: 100 + 520
2. Add the result from step 1 to the third number: (100 + 520) + 60

Step 1: Add 100 and 520
100 + 520 = 620

Step 2: Add the result from step 1 to the third number (60)
(620) + 60 = 680

So, the sum of 100, 520, and 60 is 680.
"""

BF16 Inference with Intel Extension for Transformers and Intel Extension for Pytorch

from transformers import AutoTokenizer, TextStreamer
import torch
from intel_extension_for_transformers.transformers import AutoModelForCausalLM
import intel_extension_for_pytorch as ipex

model_name = "Intel/neural-chat-7b-v3-3"
prompt = "Once upon a time, there existed a little girl,"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
model = ipex.optimize(model.eval(), dtype=torch.bfloat16, inplace=True, level="O1", auto_kernel_selection=True)

outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)

INT4 Inference with Transformers and Intel Extension for Transformers

from transformers import AutoTokenizer, TextStreamer
from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig
model_name = "Intel/neural-chat-7b-v3-3"

# for int8, should set weight_dtype="int8"       
config = WeightOnlyQuantConfig(compute_dtype="bf16", weight_dtype="int4")
prompt = "Once upon a time, there existed a little girl,"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)

model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=config)
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)

Quantitative Analyses

The Open LLM Leaderboard results can be found here: https://huggingface.co/datasets/open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3. The metrics came out to:

Ethical Considerations and Limitations

Neural-chat-7b-v3-3 can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of neural-chat-7b-v3-3, developers should perform safety testing.

Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

• Intel Neural Compressor link
• Intel Extension for Transformers link

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here