File size: 7,353 Bytes

---
license: apache-2.0
language:
- en
- zh
base_model: prithivMLmods/Primal-Opus-14B-Optimus-v2
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- sft
- llama-cpp
- gguf-my-repo
model-index:
- name: Primal-Opus-14B-Optimus-v2
  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: 64.04
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      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: 50.18
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      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: 42.07
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      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: 18.9
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      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: 21.15
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      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: 49.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FPrimal-Opus-14B-Optimus-v2
      name: Open LLM Leaderboard
---

# Triangle104/Primal-Opus-14B-Optimus-v2-Q4_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Primal-Opus-14B-Optimus-v2`](https://huggingface.co/prithivMLmods/Primal-Opus-14B-Optimus-v2) 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/Primal-Opus-14B-Optimus-v2) for more details on the model.

---

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/Primal-Opus-14B-Optimus-v2"

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 Logical Reasoning: Designed for logical deduction, multi-step problem-solving, and 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/Primal-Opus-14B-Optimus-v2-Q4_K_M-GGUF --hf-file primal-opus-14b-optimus-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Primal-Opus-14B-Optimus-v2-Q4_K_M-GGUF --hf-file primal-opus-14b-optimus-v2-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/Primal-Opus-14B-Optimus-v2-Q4_K_M-GGUF --hf-file primal-opus-14b-optimus-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Primal-Opus-14B-Optimus-v2-Q4_K_M-GGUF --hf-file primal-opus-14b-optimus-v2-q4_k_m.gguf -c 2048
```