---
license: apache-2.0
language:
- en
base_model: prithivMLmods/Jolt-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- open-llm
- math
- jolt
- llama-cpp
- gguf-my-repo
---

# Triangle104/Jolt-v0.1-Q4_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Jolt-v0.1`](https://huggingface.co/prithivMLmods/Jolt-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/Jolt-v0.1) for more details on the model.

---
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.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```