chatbench-mistral-7b

37
4
7.0B
1 language
license:apache-2.0
by
microsoft
Other
OTHER
7B params
New
37 downloads
Early-stage
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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

šŸŸ” Average (5.3/10)

Researched training datasets used by chatbench-mistral-7b with quality assessment

Specialized For

general
science
code
multilingual
reasoning

Training Datasets (4)

common crawl
šŸ”“ 2.5/10
general
science
Key Strengths
  • ā€¢Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • ā€¢Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • ā€¢Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • ā€¢Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • ā€¢Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
the pile
šŸŸ¢ 8/10
code
general
science
multilingual
Key Strengths
  • ā€¢Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • ā€¢Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • ā€¢Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
wikipedia
šŸŸ” 5/10
science
multilingual
Key Strengths
  • ā€¢High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • ā€¢Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • ā€¢Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • ā€¢Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • ā€¢Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
šŸŸ” 5.5/10
science
reasoning
Key Strengths
  • ā€¢Scientific Authority: Peer-reviewed content from established repository
  • ā€¢Domain-Specific: Specialized vocabulary and concepts
  • ā€¢Mathematical Content: Includes complex equations and notation
Considerations
  • ā€¢Specialized: Primarily technical and mathematical content
  • ā€¢English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

How to Get Startedpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base Mistral-7B with 8-bit quantization
base = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    load_in_8bit=True,
    device_map="auto"
)

# Load ChatBench LoRA adapter
model = PeftModel.from_pretrained(base, "microsoft/chatbench-mistral-7b")

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer.pad_token = tokenizer.eos_token

inputs = tokenizer(
    "[SYSTEM] You are a user.\n\n[USER] What is 2+2?\n\n[USER] ",
    return_tensors="pt"
).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
How to Get Startedpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base Mistral-7B with 8-bit quantization
base = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    load_in_8bit=True,
    device_map="auto"
)

# Load ChatBench LoRA adapter
model = PeftModel.from_pretrained(base, "microsoft/chatbench-mistral-7b")

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer.pad_token = tokenizer.eos_token

inputs = tokenizer(
    "[SYSTEM] You are a user.\n\n[USER] What is 2+2?\n\n[USER] ",
    return_tensors="pt"
).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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