metadata
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Elita-1
pipeline_tag: text-generation
library_name: transformers
tags:
- open-llm
- math
- text-generation-inference
- jolt-v0.1
model-index:
- name: Jolt-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: wis-k/instruction-following-eval
split: train
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 50.92
name: averaged accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: SaylorTwift/bbh
split: test
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 50.03
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: lighteval/MATH-Hard
split: test
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 35.88
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
split: train
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.34
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.49
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 48.74
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FJolt-v0.1
name: Open LLM Leaderboard
Jolt-v0.1
Jolt-v0.1 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a synthetic dataset based on math and cot datasets, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.
Key Improvements
- Enhanced Knowledge and Expertise: Improved mathematical reasoning, coding proficiency, and structured data processing.
- Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).
- Greater Adaptability: Better role-playing capabilities and resilience to diverse system prompts.
- Long-Context Support: Handles up to 128K tokens and generates up to 8K tokens per output.
- Multilingual Proficiency: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, and more.
Quickstart with Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Jolt-v0.1"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language models."
messages = [
{"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
Intended Use
- Advanced Reasoning & Context Understanding: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.
- Mathematical & Scientific Problem-Solving: Enhanced capabilities for calculations, theorem proving, and scientific queries.
- Code Generation & Debugging: Generates and optimizes code across multiple programming languages.
- Structured Data Analysis: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.
- Multilingual Applications: High proficiency in over 29 languages, enabling global-scale applications.
- Extended Content Generation: Supports detailed document writing, research reports, and instructional guides.
Limitations
- High Computational Requirements: Due to its 14B parameters and 128K context support, it requires powerful GPUs or TPUs for efficient inference.
- Language-Specific Variability: Performance may vary across supported languages, especially for low-resource languages.
- Potential Error Accumulation: Long-text generation can sometimes introduce inconsistencies over extended outputs.
- Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.
- Prompt Sensitivity: Outputs can depend on the specificity and clarity of the input prompt.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
| Metric | Value (%) |
|---|---|
| Average | 37.23 |
| IFEval (0-Shot) | 50.92 |
| BBH (3-Shot) | 50.03 |
| MATH Lvl 5 (4-Shot) | 35.88 |
| GPQA (0-shot) | 17.34 |
| MuSR (0-shot) | 20.49 |
| MMLU-PRO (5-shot) | 48.74 |