Instructions to use Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")
model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter

SGLang

How to use Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter with Docker Model Runner:
```
docker model run hf.co/Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Gemma-2B Fine-Tuned for Cover Letter Creation

Overview

Gemma-2B Fine-Tuned Python Model is a deep learning model based on the Gemma-2B architecture, fine-tuned specifically for Python programming tasks. This model is designed to understand Python code and assist developers by providing suggestions, completing code snippets, or offering corrections to improve code quality and efficiency.

Model Details

Model Name: Gemma-2B Fine-Tuned for Cover Letter Creation
Model Type: Deep Learning Model
Base Model: Gemma-2B
Language: Python
Task: Creating Cover Letter

How to Use

Install Gemma Python Package:

 pip install -q -U transformers==4.38.0
 pip install torch

Inference

How to use the model in our notebook:

# Load model directly
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")
model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter")

job_title = "ML Engineer"
preferred_qualification = "strong AI realted skills"
hiring_company_name = "Google"
user_name = "Vicky"
past_working_experience= "N/A"
current_working_experience = "Fresher"
skilleset= "Machine Learning, Deep Learning, AI, SQL, NLP"
qualification = "Bachelor of commerce with computer application"


prompt_template = f"<start_of_turn>user Generate Cover Letter for Role: {job_title}, \
 Preferred Qualifications: {preferred_qualification}, \
 Hiring Company: {hiring_company_name}, User Name: {user_name}, \
 Past Working Experience: {past_working_experience}, Current Working Experience: {current_working_experience}, \
 Skillsets: {skilleset}, Qualifications: {qualification} <end_of_turn>\n<start_of_turn>model"

prompt = prompt_template
encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = encodeds.to(device)


# Increase max_new_tokens if needed
generated_ids = model.generate(inputs, max_new_tokens=250, do_sample=False, pad_token_id=tokenizer.eos_token_id)
ans = ''
for i in tokenizer.decode(generated_ids[0], skip_special_tokens=True).split('<end_of_turn>')[:2]:
    ans += i

# Extract only the model's answer
model_answer = ans.split("model")[1].strip()
print(model_answer)

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for Mr-Vicky-01/Gemma2B-Finetuned-CoverLetter

Quantizations

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Gemma2B-Finetuned-CoverLetter