Instructions to use prithivMLmods/Equuleus-Opus-14B-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Equuleus-Opus-14B-Exp with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Equuleus-Opus-14B-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Equuleus-Opus-14B-Exp")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Equuleus-Opus-14B-Exp", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Equuleus-Opus-14B-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Equuleus-Opus-14B-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Equuleus-Opus-14B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Equuleus-Opus-14B-Exp

SGLang

How to use prithivMLmods/Equuleus-Opus-14B-Exp with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Equuleus-Opus-14B-Exp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Equuleus-Opus-14B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Equuleus-Opus-14B-Exp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Equuleus-Opus-14B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Equuleus-Opus-14B-Exp with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Equuleus-Opus-14B-Exp
```

Equuleus-Opus-14B-Exp / README.md

prithivMLmods

Adding Evaluation Results (#1)

fd08d5a verified over 1 year ago

preview code

Raw History Blame Contribute Delete

8.28 kB

---
license: apache-2.0
language:
- en
- de
- zh
base_model:
- prithivMLmods/Jolt-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- sft
model-index:
- name: Equuleus-Opus-14B-Exp
  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: 70.01
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      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: 48.62
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      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: 45.85
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      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: 18.23
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      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: 21.9
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      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: 48.6
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
      name: Open LLM Leaderboard
---
![22.png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65bb837dbfb878f46c77de4c%2F1vi8W4CbC_kQfhLn3Jykx.png%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L117"># **Equuleus-Opus-14B-Exp**  

Equuleus-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.  

## **Key Improvements**  
1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.  
2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.  
3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.  
4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.  
5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.  

## **Quickstart with transformers**  

Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Equuleus-Opus-14B-Exp"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "What are the key principles of general-purpose AI?"
messages = [
    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
    {"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]
```

## **Intended Use**  
1. **General-Purpose Reasoning**:  
   Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.  

2. **Educational and Informational Assistance**:  
   Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.  

3. **Conversational AI and Chatbots**:  
   Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.  

4. **Multilingual Applications**:  
   Supports global communication, translations, and multilingual content generation.  

5. **Structured Data Processing**:  
   Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.  

6. **Long-Form Content Generation**:  
   Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.  

## **Limitations**  
1. **Hardware Requirements**:  
   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  

2. **Potential Bias in Responses**:  
   While designed to be neutral, outputs may still reflect biases present in training data.  

3. **Inconsistent Outputs in Creative Tasks**:  
   May produce variable results in storytelling and highly subjective topics.  

4. **Limited Real-World Awareness**:  
   Does not have access to real-time events beyond its training cutoff.  

5. **Error Propagation in Extended Outputs**:  
   Minor errors in early responses may affect overall coherence in long-form outputs.  

6. **Prompt Sensitivity**:  
   The effectiveness of responses may depend on how well the input prompt is structured.  
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Equuleus-Opus-14B-Exp-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEquuleus-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    42.20|
|IFEval (0-Shot)    |    70.01|
|BBH (3-Shot)       |    48.62|
|MATH Lvl 5 (4-Shot)|    45.85|
|GPQA (0-shot)      |    18.23|
|MuSR (0-shot)      |    21.90|
|MMLU-PRO (5-shot)  |    48.60|

	---
	license: apache-2.0
	language:
	- en
	- de
	- zh
	base_model:
	- prithivMLmods/Jolt-v0.1
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- trl
	- sft
	model-index:
	- name: Equuleus-Opus-14B-Exp
	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: 70.01
	name: averaged accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	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: 48.62
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	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: 45.85
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	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: 18.23
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	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: 21.9
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	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: 48.6
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEquuleus-Opus-14B-Exp
	name: Open LLM Leaderboard
	---

	![22.png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65bb837dbfb878f46c77de4c%2F1vi8W4CbC_kQfhLn3Jykx.png%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L117">		# Equuleus-Opus-14B-Exp

	Equuleus-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.

	## Key Improvements
	1. Enhanced General Knowledge: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.
	2. Improved Instruction Following: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.
	3. Versatile Adaptability: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.
	4. Long-Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
	5. Multilingual Proficiency: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

	## Quickstart with transformers

	Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "prithivMLmods/Equuleus-Opus-14B-Exp"

	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	prompt = "What are the key principles of general-purpose AI?"
	messages = [
	{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
	{"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]
	```

	## Intended Use
	1. General-Purpose Reasoning:
	Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.

	2. Educational and Informational Assistance:
	Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.

	3. Conversational AI and Chatbots:
	Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.

	4. Multilingual Applications:
	Supports global communication, translations, and multilingual content generation.

	5. Structured Data Processing:
	Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.

	6. Long-Form Content Generation:
	Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.

	## Limitations
	1. Hardware Requirements:
	Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.

	2. Potential Bias in Responses:
	While designed to be neutral, outputs may still reflect biases present in training data.

	3. Inconsistent Outputs in Creative Tasks:
	May produce variable results in storytelling and highly subjective topics.

	4. Limited Real-World Awareness:
	Does not have access to real-time events beyond its training cutoff.

	5. Error Propagation in Extended Outputs:
	Minor errors in early responses may affect overall coherence in long-form outputs.

	6. Prompt Sensitivity:
	The effectiveness of responses may depend on how well the input prompt is structured.
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Equuleus-Opus-14B-Exp-details)!
	Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEquuleus-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

	\| Metric \|Value (%)\|
	\|-------------------\|--------:\|
	\|Average \| 42.20\|
	\|IFEval (0-Shot) \| 70.01\|
	\|BBH (3-Shot) \| 48.62\|
	\|MATH Lvl 5 (4-Shot)\| 45.85\|
	\|GPQA (0-shot) \| 18.23\|
	\|MuSR (0-shot) \| 21.90\|
	\|MMLU-PRO (5-shot) \| 48.60\|