MoMonir/Llama3-ChatQA-1.5-8B-GGUF

MoMonir/Phi-3-mini-128k-instruct-GGUF

This model was converted to GGUF format from nvidia/Llama3-ChatQA-1.5-8B Refer to the original model card for more details on the model.

About GGUF (TheBloke Description)

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
• candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

#--# Original Model Card #--#

Model Details

We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from ChatQA (1.0), and it is built on top of Llama-3 base model. Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using Megatron-LM, we converted the checkpoints to Hugging Face format.

Other Resources

Llama3-ChatQA-1.5-70B   Evaluation Data   Training Data   Retriever

Benchmark Results

Results in ChatRAG Bench are as follows:

	ChatQA-1.0-7B	Command-R-Plus	Llama-3-instruct-70b	GPT-4-0613	ChatQA-1.0-70B	ChatQA-1.5-8B	ChatQA-1.5-70B
Doc2Dial	37.88	33.51	37.88	34.16	38.9	39.33	41.26
QuAC	29.69	34.16	36.96	40.29	41.82	39.73	38.82
QReCC	46.97	49.77	51.34	52.01	48.05	49.03	51.40
CoQA	76.61	69.71	76.98	77.42	78.57	76.46	78.44
DoQA	41.57	40.67	41.24	43.39	51.94	49.6	50.67
ConvFinQA	51.61	71.21	76.6	81.28	73.69	78.46	81.88
SQA	61.87	74.07	69.61	79.21	69.14	73.28	83.82
TopioCQA	45.45	53.77	49.72	45.09	50.98	49.96	55.63
HybriDial*	54.51	46.7	48.59	49.81	56.44	65.76	68.27
INSCIT	30.96	35.76	36.23	36.34	31.9	30.1	32.31
Average (all)	47.71	50.93	52.52	53.90	54.14	55.17	58.25
Average (exclude HybriDial)	46.96	51.40	52.95	54.35	53.89	53.99	57.14

Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ChatRAG Bench can be found here.

Prompt Format

System: {System}

{Context}

User: {Question}

Assistant: {Response}

User: {Question}

Assistant:

How to use

take the whole document as context

This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "nvidia/Llama3-ChatQA-1.5-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

messages = [
    {"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"}
]

document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n| $ in millions, except earnings per share | Q4 FY24 | Q3 FY24 | Q4 FY23 | Q/Q | Y/Y |\n| Revenue | $22,103 | $18,120 | $6,051 | Up 22% | Up 265% |\n| Gross margin | 76.0% | 74.0% | 63.3% | Up 2.0 pts | Up 12.7 pts |\n| Operating expenses | $3,176 | $2,983 | $2,576 | Up 6% | Up 23% |\n| Operating income | $13,615 | $10,417 | $1,257 | Up 31% | Up 983% |\n| Net income | $12,285 | $9,243 | $1,414 | Up 33% | Up 769% |\n| Diluted earnings per share | $4.93 | $3.71 | $0.57 | Up 33% | Up 765% |"""

def get_formatted_input(messages, context):
    system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
    instruction = "Please give a full and complete answer for the question."

    for item in messages:
        if item['role'] == "user":
            ## only apply this instruction for the first user turn
            item['content'] = instruction + " " + item['content']
            break

    conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
    formatted_input = system + "\n\n" + context + "\n\n" + conversation
    
    return formatted_input

formatted_input = get_formatted_input(messages, document)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)

response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

run retrieval to get top-n chunks as context

This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our Dragon-multiturn retriever which can handle conversatinoal query. In addition, we provide a few documents for users to play with.

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
import torch
import json

## load ChatQA-1.5 tokenizer and model
model_id = "nvidia/Llama3-ChatQA-1.5-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

## load retriever tokenizer and model
retriever_tokenizer = AutoTokenizer.from_pretrained('nvidia/dragon-multiturn-query-encoder')
query_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-query-encoder')
context_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-context-encoder')

## prepare documents, we take landrover car manual document that we provide as an example
chunk_list = json.load(open("docs.json"))['landrover']

messages = [
    {"role": "user", "content": "how to connect the bluetooth in the car?"}
]

### running retrieval
## convert query into a format as follows:
## user: {user}\nagent: {agent}\nuser: {user}
formatted_query_for_retriever = '\n'.join([turn['role'] + ": " + turn['content'] for turn in messages]).strip()

query_input = retriever_tokenizer(formatted_query_for_retriever, return_tensors='pt')
ctx_input = retriever_tokenizer(chunk_list, padding=True, truncation=True, max_length=512, return_tensors='pt')
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]

## Compute similarity scores using dot product and rank the similarity
similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx)
ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx)

## get top-n chunks (n=5)
retrieved_chunks = [chunk_list[idx] for idx in ranked_results.tolist()[0][:5]]
context = "\n\n".join(retrieved_chunks)

### running text generation
formatted_input = get_formatted_input(messages, context)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)

response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Correspondence to

Zihan Liu (zihanl@nvidia.com), Wei Ping (wping@nvidia.com)

Citation

@article{liu2024chatqa,
  title={ChatQA: Building GPT-4 Level Conversational QA Models},
  author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
  journal={arXiv preprint arXiv:2401.10225},
  year={2024}}

License

The use of this model is governed by the META LLAMA 3 COMMUNITY LICENSE AGREEMENT