transformers gguf text-generation-inference unsloth qwen2 trl en zh fr es pt de it ru ja ko vi th ar fa he tr cs pl hi bn ur id ms lo my ceb km tl nl dataset:prithivMLmods/PyCodeZone dataset:bespokelabs/Bespoke-Stratos-17k dataset:openai/gsm8k dataset:rubenroy/GammaCorpus-v1-50k-UNFILTERED base_model:Spestly/Atlas-Pro-7B-Preview-1M base_model:quantized:Spestly/Atlas-Pro-7B-Preview-1M license:mit endpoints_compatible region:us conversational
---
base_model: Spestly/Atlas-Pro-7B-Preview-1M
datasets:
- prithivMLmods/PyCodeZone
- bespokelabs/Bespoke-Stratos-17k
- openai/gsm8k
- rubenroy/GammaCorpus-v1-50k-UNFILTERED
language:
- en
- zh
- fr
- es
- pt
- de
- it
- ru
- ja
- ko
- vi
- th
- ar
- fa
- he
- tr
- cs
- pl
- hi
- bn
- ur
- id
- ms
- lo
- my
- ceb
- km
- tl
- nl
library_name: transformers
license: mit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
quantized_by: mradermacher
extra_gated_prompt: By accessing this model, you agree to comply with ethical usage
  guidelines and accept full responsibility for its applications. You will not use
  this model for harmful, malicious, or illegal activities, and you understand that
  the model's use is subject to ongoing monitoring for misuse. This model is provided
  'AS IS' and agreeing to this means that you are responsible for all the outputs
  generated by you
extra_gated_fields:
  Name: text
  Organization: text
  Country: country
  Date of Birth: date_picker
  Intended Use:
    type: select
    options:
    - Research
    - Education
    - Personal Development
    - Commercial Use
    - label: Other
      value: other
  I agree to use this model in accordance with all applicable laws and ethical guidelines: checkbox
  I agree to use this model under the MIT licence: checkbox
---
![Header](https://huggingface.co/Spestly/Atlas-Pro-7B-Preview-1M/resolve/main/Atlas-Pro-1M.png)

## **Spestly/Atlas-Pro-7B-Preview-1M**

### **Model Overview**  
**Atlas-Pro-7B-Preview-1M** is a fine-tuned version of the **Qwen2.5-7B-Instruct-1M** model, tailored for superior performance in general-purpose question answering and reasoning tasks. This model focuses on delivering clear, concise answers while maintaining a natural, conversational tone. By incorporating subtle grammatical imperfections, it creates a more relatable and human-like interaction style.  

**Key Features:**  
- **Enhanced Reasoning Capabilities:** Fine-tuning has improved the model's ability to handle reasoning-focused questions with better accuracy and depth.  
- **Humanized Interaction:** Subtle grammar imperfections are included intentionally to emulate a more human-like conversational experience.  
- **Improved QA Performance:** Extensive training has refined the model's ability to respond to questions accurately and contextually.  

---

### **Model Details**  
- **Base Model:** [Qwen/Qwen2.5-7B-Instruct-1M](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M)  
- **Fine-Tuned Dataset:** A carefully curated mix of instructional and conversational data, designed to improve reasoning and question-answering performance.  
- **Parameter Count:** 7 billion (7B)  
- **Architecture:** Transformer-based, leveraging the Qwen2.5 architecture for high efficiency and accuracy.
- **Context Window**: **1 Million Tokens**

---

### **Training Procedure**  
The model was fine-tuned using the following strategies:  
- **Dataset Quality:** A diverse dataset was selected (Public and Private), focusing on improving reasoning and conversational understanding.  
- **Humanization:** Data augmentation techniques were employed to add slight grammar imperfections, mimicking human language patterns.  
- **Optimization:** Training was conducted using mixed-precision techniques to ensure efficiency without compromising performance.  

---

### **Limitations**  
While the model excels in reasoning and answering questions, it:  
- May produce occasional inaccuracies if provided with ambiguous or incomplete queries.  
- Does not specialize in niche technical domains or highly specific knowledge areas outside its training data.  
- Subtle grammatical errors are intentional and may occasionally appear in unintended contexts.  

---

### **Usage**  
The model can be used for:  
- Interactive chatbots with a humanized tone.  
- General-purpose reasoning and question-answering tasks.  
- Personal assistant tools designed for natural communication.  

---

### **Example Usage**  

#### Basic: Ollama + LM Studio
I recommend that you use [**LM Studio**](https://lmstudio.ai/). Later down the Atlas development, Alternative you can also run it via Ollama with this Ollama command:

```bash
ollama run hf.co/Spestly/Atlas-Pro-7B-Preview-1M-GGUF:IQ4_XS
```

Remember to replace the tag at the end with the Quant you want to use

#### Advanced: TGI (Text Generation Interface):

>> WARNING!: Only use this method if you have experience using TGI.

First you need to start the TGI server via this command (Make sure you have docker installed):

```bash
# Deploy with docker on Linux:
docker run --gpus all \
	-v ~/.cache/huggingface:/root/.cache/huggingface \
 	-e HF_TOKEN="<secret>" \
	-p 8000:80 \
	ghcr.io/huggingface/text-generation-inference:latest \
	--model-id Spestly/Atlas-Pro-7B-Preview-1M-GGUF
```

You now call the server you just deployed!

```bas
# Call the server using curl:
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Spestly/Atlas-Pro-7B-Preview-1M-GGUF",
		"messages": [
			{"role": "user", "content": "What is the capital of France?"}
		]
	}'
```

---

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q2_K.gguf) | Q2_K | 3.1 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q3_K_S.gguf) | Q3_K_S | 3.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q3_K_L.gguf) | Q3_K_L | 4.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.IQ4_XS.gguf) | IQ4_XS | 4.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q5_K_S.gguf) | Q5_K_S | 5.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q5_K_M.gguf) | Q5_K_M | 5.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Atlas-Pro-7B-Preview-1M-GGUF/resolve/main/Atlas-Pro-7B-Preview-1M.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |

---

### **Community**  
We encourage feedback and contributions from the community. Please report any issues or suggest improvements via the [model’s Hugging Face page](https://huggingface.co/Spestly/Atlas-Pro-7B-Preview-1M).  

**License:** MIT

**Contact:** For questions or collaboration opportunities, please reach out via [Hugging Face](https://huggingface.co/Spestly).  

---