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--- |
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base_model: Spestly/Atlas-Flash-7B-Preview |
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tags: |
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- text-generation-inference |
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- transformers |
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- qwen2 |
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- trl |
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- r1 |
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- gemini-2.0 |
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- gpt4 |
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- conversational |
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- chat |
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- llama-cpp |
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- gguf-my-repo |
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license: mit |
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language: |
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- en |
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- zh |
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- fr |
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- es |
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- pt |
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- de |
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- it |
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- ru |
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- ja |
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- ko |
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- vi |
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- th |
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- ar |
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- fa |
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- he |
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- tr |
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- cs |
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- pl |
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- hi |
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- bn |
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- ur |
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- id |
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- ms |
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- lo |
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- my |
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- ceb |
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- km |
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- tl |
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- nl |
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library_name: transformers |
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datasets: |
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- BAAI/TACO |
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- codeparrot/apps |
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- rubenroy/GammaCorpus-v1-70k-UNFILTERED |
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extra_gated_prompt: By accessing this model, you agree to comply with ethical usage |
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guidelines and accept full responsibility for its applications. You will not use |
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this model for harmful, malicious, or illegal activities, and you understand that |
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the model's use is subject to ongoing monitoring for misuse. This model is provided |
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'AS IS' and agreeing to this means that you are responsible for all the outputs |
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generated by you |
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extra_gated_fields: |
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Name: text |
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Organization: text |
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Country: country |
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Date of Birth: date_picker |
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Intended Use: |
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type: select |
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options: |
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- Research |
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- Education |
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- Personal Development |
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- Commercial Use |
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- label: Other |
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value: other |
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I agree to use this model in accordance with all applicable laws and ethical guidelines: checkbox |
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I agree to use this model under the MIT licence: checkbox |
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--- |
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# Triangle104/Atlas-Flash-7B-Preview-Q8_0-GGUF |
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This model was converted to GGUF format from [`Spestly/Atlas-Flash-7B-Preview`](https://huggingface.co/Spestly/Atlas-Flash-7B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/Spestly/Atlas-Flash-7B-Preview) for more details on the model. |
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--- |
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Atlas-Flash is the first model in the Atlas family, a new generation of AI systems designed to excel in tasks requiring advanced reasoning, contextual understanding, and domain-specific expertise. Built on Deepseek's R1 distilled Qwen models, Atlas-Flash integrates state-of-the-art methodologies to deliver significant improvements in coding, conversational AI, and STEM problem-solving. |
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With a focus on versatility and robustness, Atlas-Flash adheres to the core principles established in the Athena project, emphasizing transparency, fairness, and responsible AI development. |
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Model Details |
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Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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Parameters: 7 Billion |
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License: MIT |
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Key Features |
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Improved Coding Capabilities |
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Supports accurate and efficient code generation, debugging, code explanation, and documentation writing. |
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Handles multiple programming languages and frameworks with strong contextual understanding. |
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Excels at solving algorithmic problems and generating optimized solutions for software development tasks. |
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Advanced Conversational Skills |
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Provides natural, context-aware, and coherent multi-turn dialogue. |
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Handles both informal chat and task-specific queries with adaptability. |
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Can summarize, clarify, and infer meaning from conversational input, enabling dynamic interaction. |
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Proficiency in STEM Domains |
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Excels in solving complex problems in mathematics, physics, and engineering. |
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Capable of explaining intricate concepts with clarity, making it a useful tool for education and technical research. |
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Demonstrates strong reasoning skills in tasks requiring logic, pattern recognition, and domain-specific expertise. |
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Training Details |
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Atlas-Flash underwent extensive training on a diverse set of high-quality datasets to ensure broad domain coverage and exceptional performance. The training process prioritized both generalization and specialization, leveraging curated data for coding, conversational AI, and STEM-specific tasks. |
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Datasets Used: |
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BAAI/TACO |
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A robust natural language dataset designed for language understanding and contextual reasoning. |
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Enables the model to excel in tasks requiring deep comprehension and nuanced responses. |
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rubenroy/GammaCorpus-v1-70k-UNFILTERED |
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A large-scale, unfiltered corpus that provides a diverse range of real-world language examples. |
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Ensures the model can handle informal, technical, and domain-specific language effectively. |
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codeparrot/apps |
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A dataset built for programming tasks, covering a wide range of coding challenges, applications, and practical use cases. |
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Ensures high performance in software development tasks, including debugging, optimization, and code explanation. |
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Hand-Collected Synthetic Data |
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Curated datasets tailored to specific tasks for fine-tuning and specialization. |
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Includes challenging edge cases and rare scenarios to improve model adaptability and resilience. |
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Training Methodology |
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Distillation from Qwen Models: Atlas-Flash builds on Deepseek's distilled Qwen models, inheriting their strengths in language understanding and multi-domain reasoning. |
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Multi-Stage Training: The training process included multiple stages of fine-tuning, focusing separately on coding, general language tasks, and STEM domains. |
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Synthetic Data Augmentation: Hand-collected synthetic datasets were used to supplement real-world data, ensuring the model is capable of handling corner cases and rare scenarios. |
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Iterative Feedback Loop: Performance was iteratively refined through evaluation and feedback, ensuring robust and accurate outputs across tasks. |
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Applications |
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Atlas-Flash is designed for a wide range of use cases: |
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1. Software Development |
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Code generation, optimization, and debugging. |
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Explaining code logic and writing documentation. |
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Automating repetitive tasks in software engineering workflows. |
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2. Conversational AI |
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Building intelligent chatbots and virtual assistants. |
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Providing context-aware, coherent, and natural multi-turn dialogue. |
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Summarizing conversations and supporting decision-making in interactive systems. |
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3. STEM Problem-Solving |
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Solving mathematical problems with step-by-step explanations. |
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Assisting with physics, engineering, and data analysis tasks. |
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Supporting scientific research through technical insights and reasoning. |
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4. Education and Knowledge Assistance |
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Simplifying and explaining complex concepts for learners. |
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Acting as a virtual tutor for coding and STEM disciplines. |
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Providing accurate answers to general knowledge and domain-specific queries. |
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Strengths |
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Versatility: Performs exceptionally well across multiple domains, including coding, conversational AI, and STEM tasks. |
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Contextual Understanding: Handles nuanced and multi-turn interactions with strong comprehension. |
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High Accuracy: Delivers precise results for complex coding and STEM challenges. |
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Adaptability: Capable of generating creative and optimized solutions for diverse use cases. |
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Limitations |
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While Atlas-Flash demonstrates significant advancements, it has the following limitations: |
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Bias in Training Data: Despite efforts to curate high-quality datasets, biases in the training data may occasionally influence outputs. |
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Context Length Constraints: The model may struggle with extremely long documents or conversations that exceed its maximum context window. |
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Domain-Specific Knowledge Gaps: While Atlas-Flash is versatile, it may underperform in highly niche or specialized domains that were not sufficiently represented in the training data. |
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Dependence on Input Quality: The model's performance depends on the clarity and coherence of the input provided by the user. |
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Ethical Considerations |
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Misuse Prevention: Users are expected to employ Atlas-Flash responsibly and avoid applications that could cause harm or violate ethical guidelines. |
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Transparency and Explainability: Efforts have been made to ensure the model provides clear and explainable outputs, particularly for STEM and coding tasks. |
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Bias Mitigation: While biases have been minimized during training, users should remain cautious and critically evaluate outputs for fairness and inclusivity. |
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Future Directions |
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As the first model in the Atlas family, Atlas-Flash establishes a strong foundation for future iterations. Planned improvements include: |
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Expanded Training Data: Integration of more diverse and niche datasets to address knowledge gaps. |
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Improved Context Management: Enhancements in handling long-context tasks and multi-turn conversations. |
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Domain-Specific Fine-Tuning: Specialization in areas such as healthcare, legal, and advanced scientific research. |
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Atlas-Pro: Atlas-Pro is meant to be built on Atlas-Flash to provide excellent reasoning when answering questions |
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Conclusion |
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Atlas-Flash is a versatile and robust model that sets new benchmarks in coding, conversational AI, and STEM problem-solving. By leveraging Deepseek's R1 distilled Qwen models and high-quality datasets, it offers exceptional performance across a wide range of tasks. As the first model in the Atlas family, it represents a significant step forward, laying the groundwork for future innovations in AI development. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Atlas-Flash-7B-Preview-Q8_0-GGUF --hf-file atlas-flash-7b-preview-q8_0.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Atlas-Flash-7B-Preview-Q8_0-GGUF --hf-file atlas-flash-7b-preview-q8_0.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Atlas-Flash-7B-Preview-Q8_0-GGUF --hf-file atlas-flash-7b-preview-q8_0.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Atlas-Flash-7B-Preview-Q8_0-GGUF --hf-file atlas-flash-7b-preview-q8_0.gguf -c 2048 |
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``` |
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