Mistral-7b-Indic-Chat-LoRA-v1
1
license:cc-by-nc-4.0
by
Neural-Hacker
Language Model
OTHER
7B params
New
0 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM
Training Data Analysis
🟡 Average (5.3/10)
Researched training datasets used by Mistral-7b-Indic-Chat-LoRA-v1 with quality assessment
Specialized For
general
science
code
multilingual
reasoning
Training Datasets (4)
common crawl
🔴 2.5/10
general
science
Key Strengths
- •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
- •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
- •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
- •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
- •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
- •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
- •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
- •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
- •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
- •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
- •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
- •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
- •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
- •Scientific Authority: Peer-reviewed content from established repository
- •Domain-Specific: Specialized vocabulary and concepts
- •Mathematical Content: Includes complex equations and notation
Considerations
- •Specialized: Primarily technical and mathematical content
- •English-Heavy: Predominantly English-language papers
Explore our comprehensive training dataset analysis
View All DatasetsCode Examples
Datasettexttransformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(base_model, "YOUR_MODEL_PATH")
model.eval()
prompt = "User: gravity kya hoti hai?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=50,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.2,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("Assistant:")[-1].strip())Deploy This Model
Production-ready deployment in minutes
Together.ai
Instant API access to this model
Production-ready inference API. Start free, scale to millions.
Try Free APIReplicate
One-click model deployment
Run models in the cloud with simple API. No DevOps required.
Deploy NowDisclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.