Thinking-Camel-7b is a 7 billion parameter large language model fine-tuned from ALLaM-7B-Instruct-preview. This model is designed to enhance reasoning capabilities while maintaining the core strengths of the ALLAM architecture.
Model Details
Developed by: Mohaddz
Model type: Causal Language Model
Language(s): English (primary), with potential capabilities in other languages supported by the base model
License: [Same as base model - apache-2.0]
Base model: ALLAM-7b-Instruct
Training paradigm: GRPO
Intended Uses
Thinking-Camel-7b is intended for a variety of applications requiring strong reasoning capabilities, including but not limited to:
Complex problem-solving
Step-by-step reasoning for mathematical and logical problems
You can use Thinking-Camel-7b with the Hugging Face Transformers library:
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
23# Load model and tokenizer4tokenizer = AutoTokenizer.from_pretrained("Mohaddz/Thinking-Camel-7b")5model = AutoModelForCausalLM.from_pretrained("Mohaddz/Thinking-Camel-7b")67# Generate text8inputs = tokenizer("Question: What would happen if we doubled the Earth's gravity? Think through this step by step.", return_tensors="pt")9outputs = model.generate(**inputs, max_length=512)10response = tokenizer.decode(outputs[0], skip_special_tokens=True)11print(response)
Using with vLLM for faster inference:
python
1from vllm import LLM, SamplingParams
23# Initialize the model4llm = LLM(model="Mohaddz/Thinking-Camel-7b")56# Set sampling parameters7sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512)89# Generate completions10prompts =["Question: How would you solve the Tower of Hanoi problem with 3 disks? Think step by step."]11outputs = llm.generate(prompts, sampling_params)1213# Print the generated text14for output in outputs:15print(output.outputs[0].text)
Prompt Format
Thinking-Camel-7b works best with prompts that explicitly ask the model to think step by step:
Question: [Your complex problem or question here]
Think through this step by step.
For general use, you can also use a standard instruction format:
[Instruction or question]
Limitations
As with all LLMs, Thinking-Camel-7b may occasionally generate factually incorrect information
The model inherits limitations from its base model, ALLAM-7b-Instruct
Benchmarks are not yet available to quantify performance improvements
Like most models in this size range, it may struggle with highly specialized domain knowledge
Performance on complex reasoning tasks may vary
Training
Thinking-Camel-7b was fine-tuned from ALLaM-7B-Instruct-preview with a focus on enhancing reasoning capabilities. The training approach prioritized:
Chain-of-thought examples
Step-by-step problem solving
Improved logical reasoning structures
Explicit thinking processes
Ethical Considerations
Users should be aware of common LLM limitations including potential biases inherited from the training data, hallucinations, and the need for human oversight particularly in sensitive applications. This model should not be used as the sole decision-maker for critical applications.
Future Work
Comprehensive benchmarking across standard LLM evaluation suites
Further fine-tuning on specialized reasoning tasks
Potential instruction-tuning with human feedback
Citation
If you use this model in your research, please cite:
@misc{mohaddz2025thinkingcamel,
author = {Mohaddz},
title = {Thinking-Camel-7b},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Mohaddz/Thinking-Camel-7b}}
}
Contact
For questions, feedback, or issues related to Thinking-Camel-7b, please contact Mohaddz through Hugging Face or open an issue in the model repository.
