You can deploy this model and start earning money today!

ujjwal52/
Llama-2-7b-chat-UK-SPIN

Name: Llama-2-7b-chat-UK-SPIN
Availability: InStock

safetensors

llama

ujjwal52/Llama-2-7b-chat-finetune-UK-Adv

quantized

4-bit

bitsandbytes

Views

No views yet

ujjwal52/Llama-2-7b-chat-UK-SPIN

Model Description

This is a 4-bit quantized version of the ujjwal52/Llama-2-7b-chat-finetune-UK-Adv model, which was further fine-tuned using the Self-Play finetuning on Instruct (SPIN) approach( Fast APPROCH ). The original base model, Llama-2-7b-chat, was fine-tuned with data to specialize in generating responses relevant to the various context. This version benefits from 4-bit quantization, significantly reducing its memory footprint and enabling efficient deployment on resource-constrained environments, while maintaining much of its performance.

Intended Use

This model is primarily intended for:

specific Chat and Conversational AI: Generating human-like responses in a chat context, with a particular emphasis on exceptional knowledge knowledge.
Text Generation: Creating coherent and contextually relevant text.
Humanized and Quick Advisory (Fast Context): Providing preliminary information or responses related to any topics. (Disclaimer: This model is for informational purposes only and should not be used as a substitute for professional advice.)
Research and Development: Exploring the capabilities of 4-bit quantized Llama-2 models and the impact of SPIN fine-tuning on domain-specific datasets.

Training Data

This model was built upon:

Llama-2-7b-chat: Trained on a diverse range of publicly available online data.
specific Rapid Response: Fine-tuned on a dataset tailored to provide domain-specific knowledge relevant to the usecase landscape.

Training Procedure & Quantization

Fine-tuning

The model was initially fine-tuned from Llama-2-7b-chat using legal , financial, advisory,chat data. It then underwent further refinement using the **Self-Play finetuning on Instruct ** method, which allows the model to learn from its own generated responses and iteratively improve its instruction-following capabilities and response quality.

Quantization

The model size from its original full-precision size (which can be over 30 GB) or approximately 13 GB (float16) to roughly 3.9 GB, making it only 4GB in size and much more accessible for deployment on consumer hardware and cloud environments with limited GPU memory.

How to Use

To load and use this model with the transformers library, you can follow this example:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# Define the model path on Hugging Face Hub
model_name = "ujjwal52/Llama-2-7b-chat-UK-SPIN"

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=False
)

# Load the 4-bit quantized model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example for text generation (using pipeline for simplicity)
from transformers import pipeline

prompt = "<s>[INST] What are the key differences between a solicitor and a barrister in the UK legal system? [/INST]"

pipeline = pipeline(
    task="text-generation", 
    model=model, 
    tokenizer=tokenizer,
    max_new_tokens=500
)

result = pipeline(prompt)
print(result[0]['generated_text'])

# Clean up memory (optional)
del model
del tokenizer
import gc
import torch
gc.collect()
torch.cuda.empty_cache()

Example Generation

python

# Assuming the model and tokenizer are loaded as above

prompt = "<s>[INST] Write a short explanation of the UK's 'Right to Roam' law. [/INST]"

pipeline = pipeline(
    task="text-generation", 
    model=model, 
    tokenizer=tokenizer,
    max_new_tokens=200
)

result = pipeline(prompt)
print(result[0]['generated_text'])

Expected Output (example, actual output may vary):

<s>[INST] Write a short explanation of the UK's 'Right to Roam' law. [/INST] The 'Right to Roam' in the UK, formally known as the right of access to 'access land' under the Countryside and Rights of Way Act 2000 (CRoW Act), grants the public the right to walk freely over certain types of privately-owned land without needing to stick to paths. This includes mountains, moorland, heathland, downland, and registered common land. 

However, this right comes with responsibilities and some limitations. For instance, you generally can't take vehicles onto access land, disturb wildlife, or damage property. There are also exclusions, such as cultivated land, gardens, and military byelaw land. Landowners can temporarily close areas for land management or safety reasons, and there are specific rules about dogs on access land.

In Scotland, the Land Reform (Scotland) Act 2003 grants a broader 'right of responsible access' to most land and inland water, provided it's exercised responsibly, respecting others' privacy, safety, and the environment. This is often seen as a more expansive right than that in England and Wales.

Limitations and Biases

Potential Biases: As with all language models, this model may inherit biases present in its training data (both the original Llama-2-7b-chat and the ). Users should be aware of and test for potential biases in outputs.
Factual Accuracy: While fine-tuned on specific domains, the model may still generate inaccurate or hallucinated information. Always verify critical outputs with reliable sources, especially for legal or financial advice.
Language: Primarily optimized for English, specifically with a UK context. Performance on other languages may be limited.
Context Window: Limited by its original architecture's context window. Complex or lengthy inputs might lead to degraded performance.
Quantization Impact: While 4-bit quantization significantly reduces size, it can introduce a marginal loss in precision compared to the full-precision model.

Acknowledgements

This model is based on the Llama-2-7b-chat model by Meta AI and further fine-tuned with data. The quantization was performed using the bitsandbytes library.