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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Condor-Opus-14B-Exp")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Condor-Opus-14B-Exp")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Condor-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/Condor-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/Condor-Opus-14B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use prithivMLmods/Condor-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/Condor-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/Condor-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/Condor-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/Condor-Opus-14B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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

prithivMLmods

Adding Evaluation Results (#1)

bf83157 verified about 1 year ago

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	---
	license: apache-2.0
	language:
	- en
	- zh
	base_model:
	- prithivMLmods/Pegasus-Opus-14B-Exp
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- trl
	- sft
	- limo
	model-index:
	- name: Condor-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: 40.43
	name: averaged accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-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: 44.08
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-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: 52.27
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-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: 18.9
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-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: 25.42
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-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: 44.6
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCondor-Opus-14B-Exp
	name: Open LLM Leaderboard
	---

	![fdgzdfdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/GBHEiwxkSXDbUt_UGtofj.png)
	# Condor-Opus-14B-Exp

	> Condor-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/Condor-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__Condor-Opus-14B-Exp-details)!
	Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCondor-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

	\| Metric \|Value (%)\|
	\|-------------------\|--------:\|
	\|Average \| 37.62\|
	\|IFEval (0-Shot) \| 40.43\|
	\|BBH (3-Shot) \| 44.08\|
	\|MATH Lvl 5 (4-Shot)\| 52.27\|
	\|GPQA (0-shot) \| 18.90\|
	\|MuSR (0-shot) \| 25.42\|
	\|MMLU-PRO (5-shot) \| 44.60\|