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RichardErkhov/
devshaheen_-_Llama-2-7b-chat-finetune-gguf

Name: devshaheen_-_Llama-2-7b-chat-finetune-gguf
Availability: InStock

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Llama-2-7b-chat-finetune - GGUF

Model creator: https://huggingface.co/devshaheen/
Original model: https://huggingface.co/devshaheen/Llama-2-7b-chat-finetune/

Name	Quant method	Size
Llama-2-7b-chat-finetune.Q2_K.gguf	Q2_K	2.36GB
Llama-2-7b-chat-finetune.IQ3_XS.gguf	IQ3_XS	2.6GB
Llama-2-7b-chat-finetune.IQ3_S.gguf	IQ3_S	2.75GB
Llama-2-7b-chat-finetune.Q3_K_S.gguf	Q3_K_S	2.75GB
Llama-2-7b-chat-finetune.IQ3_M.gguf	IQ3_M	2.9GB
Llama-2-7b-chat-finetune.Q3_K.gguf	Q3_K	3.07GB
Llama-2-7b-chat-finetune.Q3_K_M.gguf	Q3_K_M	3.07GB
Llama-2-7b-chat-finetune.Q3_K_L.gguf	Q3_K_L	3.35GB
Llama-2-7b-chat-finetune.IQ4_XS.gguf	IQ4_XS	3.4GB
Llama-2-7b-chat-finetune.Q4_0.gguf	Q4_0	3.56GB
Llama-2-7b-chat-finetune.IQ4_NL.gguf	IQ4_NL	3.58GB
Llama-2-7b-chat-finetune.Q4_K_S.gguf	Q4_K_S	3.59GB
Llama-2-7b-chat-finetune.Q4_K.gguf	Q4_K	3.8GB
Llama-2-7b-chat-finetune.Q4_K_M.gguf	Q4_K_M	3.8GB
Llama-2-7b-chat-finetune.Q4_1.gguf	Q4_1	3.95GB
Llama-2-7b-chat-finetune.Q5_0.gguf	Q5_0	4.33GB
Llama-2-7b-chat-finetune.Q5_K_S.gguf	Q5_K_S	4.33GB
Llama-2-7b-chat-finetune.Q5_K.gguf	Q5_K	4.45GB
Llama-2-7b-chat-finetune.Q5_K_M.gguf	Q5_K_M	4.45GB
Llama-2-7b-chat-finetune.Q5_1.gguf	Q5_1	4.72GB
Llama-2-7b-chat-finetune.Q6_K.gguf	Q6_K	5.15GB
Llama-2-7b-chat-finetune.Q8_0.gguf	Q8_0	6.67GB

Original model description:

license: mit datasets:

mlabonne/guanaco-llama2-1k language:
en base_model:
NousResearch/Llama-2-7b-chat-hf pipeline_tag: text-generation library_name: transformers finetuned_model: true model_type: causal-lm finetuned_task: instruction-following tags:
instruction-following
text-generation
fine-tuned
llama2
causal-language-model
QLoRa
4-bit-quantization
low-memory
training-optimized metrics:
accuracy
loss

Llama-2-7B-Chat Fine-Tuned Model

This model is a fine-tuned version of Llama-2-7B-Chat model, optimized for instruction-following tasks. It has been trained on the mlabonne/guanaco-llama2-1k dataset and is optimized for efficient text generation across various NLP tasks, including question answering, summarization, and text completion.

Model Details

Base Model: NousResearch/Llama-2-7b-chat-hf
Fine-Tuning Task: Instruction-following
Training Dataset: mlabonne/guanaco-llama2-1k
Optimized For: Text generation, question answering, summarization, and more.
Fine-Tuned Parameters:
- LoRA (Low-Rank Adaption) applied for efficient training with smaller parameter updates.
- Quantized to 4-bit for memory efficiency and better GPU utilization.
- Training includes gradient accumulation, gradient checkpointing, and weight decay to prevent overfitting and enhance memory efficiency.

Usage

You can use this fine-tuned model with the Hugging Face transformers library. Below is an example of how to load and use the model for text generation.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")
model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")

# Example text generation
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

RichardErkhov/devshaheen_-_Llama-2-7b-chat-finetune-gguf

Original model description:

Llama-2-7B-Chat Fine-Tuned Model

Model Details

Usage

RichardErkhov/
devshaheen_-_Llama-2-7b-chat-finetune-gguf