Llama-3.1-Hawkish-8B

678
48
1 language
llama
by
mukaj
Language Model
OTHER
8B params
New
678 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

Model has been further finetuned on a set of newly generated 50m high quality tokens related to Financial topics covering topics such as Economics, Fixed Income...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by Llama-3.1-Hawkish-8B with quality assessment

Specialized For

general
science
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...
c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering
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 Datasets

Code Examples

Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)
Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)
Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)
Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)
Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)
Model Usage Examplepythonvllm
from vllm import LLM, SamplingParams

# Load the LLM using vLLM
llm = LLM(model="mukaj/Llama-3.1-Hawkish-8B", dtype="auto")
tokenizer = llm.get_tokenizer()

messages = [
    {"role": "system", "content": "You are a Financial Analyst. Reason step by step before answering."},
    {"role": "user", "content": """Given that an equal-weighted index and a market-capitalization-weighted index consist of the same securities, underperformance by small-cap stocks will most likely result in the market-capitalization-weighted index exhibiting what price returns compared to the equal weighted index?""" }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Sampling configuration for vLLM
sampling_params = SamplingParams(temperature=0.2, max_tokens=512)

# Generate response using vLLM
generation = llm.generate(prompt, sampling_params)

# Decode response
generated_response = generation[0].outputs[0].text
print(generated_response)

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