Triangle104/Jolt-v0.1-Q4_K_M-GGUF

This model was converted to GGUF format from prithivMLmods/Jolt-v0.1 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Jolt-v0.1-Q4_K_M-GGUF --hf-file jolt-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Jolt-v0.1-Q4_K_M-GGUF --hf-file jolt-v0.1-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Jolt-v0.1-Q4_K_M-GGUF --hf-file jolt-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Jolt-v0.1-Q4_K_M-GGUF --hf-file jolt-v0.1-q4_k_m.gguf -c 2048