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README.md

@@ -0,0 +1,264 @@
+---
+license: other
+license_name: aml
+language:
+- en
+- es
+- fr
+- de
+- it
+- pt
+- ru
+- ar
+- hi
+- ko
+- zh
+library_name: transformers
+extra_gated_prompt: Company name is optional, please put NA if you would prefer not to share it.
+extra_gated_fields:
+  Company: text
+  I agree to use this model in accordance with the Arcee Model License (AML): checkbox
+base_model:
+- arcee-ai/AFM-4.5B-Base
+---
+
+# <span style="color: #7FFF7F;">AFM-4.5B GGUF Models</span>
+
+
+## <span style="color: #7F7FFF;">Model Generation Details</span>
+
+This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`8ad7b3e6`](https://github.com/ggerganov/llama.cpp/commit/8ad7b3e65b5834e5574c2f5640056c9047b5d93b).
+
+
+
+
+
+---
+
+## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>
+
+I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.
+
+In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:  
+👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)
+
+While this does increase model file size, it significantly improves precision for a given quantization level.
+
+### **I'd love your feedback—have you tried this? How does it perform for you?**
+
+
+
+
+---
+
+<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
+  Click here to get info on choosing the right GGUF model format
+</a>
+
+---
+
+
+
+<!--Begin Original Model Card-->
+
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/Lj9YVLIKKdImV_jID0A1g.png" width="25%" alt="Arcee AFM 4.5B">
+  </picture>
+</div>
+
+
+# AFM-4.5B
+
+AFM-4.5B is a 4.5 billion parameter instruction-tuned model developed by Arcee.ai, designed for enterprise-grade performance across diverse deployment environments from cloud to edge. The base model was trained on a dataset of 8 trillion tokens, comprising 6.5 trillion tokens of general pretraining data followed by 1.5 trillion tokens of midtraining data with enhanced focus on mathematical reasoning and code generation. Following pretraining, the model underwent supervised fine-tuning on high-quality instruction datasets. The instruction-tuned model was further refined through reinforcement learning on verifiable rewards as well as for human preference. We use a modified version of [TorchTitan](https://arxiv.org/abs/2410.06511) for pretraining, [Axolotl](https://axolotl.ai) for supervised fine-tuning, and a modified version of [Verifiers](https://github.com/willccbb/verifiers) for reinforcement learning.
+
+The development of AFM-4.5B prioritized data quality as a fundamental requirement for achieving robust model performance. We collaborated with DatologyAI, a company specializing in large-scale data curation. DatologyAI's curation pipeline integrates a suite of proprietary algorithms—model-based quality filtering, embedding-based curation, target distribution-matching, source mixing, and synthetic data. Their expertise enabled the creation of a curated dataset tailored to support strong real-world performance.
+
+The model architecture follows a standard transformer decoder-only design based on Vaswani et al., incorporating several key modifications for enhanced performance and efficiency. Notable architectural features include grouped query attention for improved inference efficiency and ReLU^2 activation functions instead of SwiGLU to enable sparsification while maintaining or exceeding performance benchmarks.
+
+The model available in this repo is the instruct model following supervised fine-tuning and reinforcement learning.
+
+***
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
+  </picture>
+</div>
+
+## Model Details
+
+* **Model Architecture:** ArceeForCausalLM
+* **Parameters:** 4.5B
+* **Training Tokens:** 
+* **License:** [Arcee Model License (AML)](https://huggingface.co/arcee-ai/AFM-4.5B#license)
+* **Recommended settings:**
+    * temperature: 0.5
+    * top_k: 50
+    * top_p: 0.95
+    * repeat_penalty: 1.1
+
+***
+
+## Benchmarks
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/BdsWFc4pxiHlK2E0j9AfG.png)
+*Qwen3 and SmolLM's reasoning approach causes their scores to vary wildly from suite to suite - but these are all scores on our internal harness with the same hyperparameters. Be sure to reference their reported scores. SmolLM just released its [bench](https://github.com/huggingface/smollm).
+
+## How to use with `transformers`
+
+You can use the model directly with the `transformers` library.
+
+We recommend a lower temperature, around 0.5, for optimal performance.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "arcee-ai/AFM-4.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.5,
+    top_k=50,
+    top_p=0.95
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## How to use with `vllm`
+
+Pending a PR merge: https://github.com/vllm-project/vllm/pull/21725
+
+## How to use with Together API
+
+You can access this model directly via the [Together Playground](https://api.together.xyz/playground/arcee-ai/AFM-4.5B). 
+
+### Python (Official Together SDK)
+
+```python
+from together import Together
+
+client = Together()
+response = client.chat.completions.create(
+    model="arcee-ai/AFM-4.5B",
+    messages=[
+        {
+            "role": "user",
+            "content": "What are some fun things to do in New York?"
+        }
+    ]
+)
+print(response.choices[0].message.content)
+```
+
+### cURL
+
+```bash
+curl -X POST "https://api.together.xyz/v1/chat/completions" \
+  -H "Authorization: Bearer $TOGETHER_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "arcee-ai/AFM-4.5B",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What are some fun things to do in New York?"
+      }
+    ]
+  }'
+```
+
+
+## Quantization support
+
+Support for llama.cpp is available, GGUF format quants are provided here:
+
+https://huggingface.co/arcee-ai/AFM-4.5B-GGUF
+
+## License
+
+AFM-4.5B is released under the [Arcee Model License](https://huggingface.co/arcee-ai/AFM-4.5B/blob/main/LICENSE). If your company makes less than $1.75 million in annual revenue, you’re free to use the model for commercial purposes, as long as you’re not providing the weights to a company above that threshold. If your product or application using AFM-4.5B is sold to a larger company, that’s fine—as long as they don’t receive or run the weights directly.
+
+We want as many developers, researchers, and builders as possible to benefit from AFM-4.5B. At the same time, this license ensures that we can continue to develop and support the model for the community.
+
+
+<!--End Original Model Card-->
+
+---
+
+# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
+
+Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  
+
+👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  
+
+
+The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)
+
+💬 **How to test**:  
+ Choose an **AI assistant type**:  
+   - `TurboLLM` (GPT-4.1-mini)  
+   - `HugLLM` (Hugginface Open-source models)  
+   - `TestLLM` (Experimental CPU-only)  
+
+### **What I’m Testing**  
+I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
+- **Function calling** against live network services  
+- **How small can a model go** while still handling:  
+  - Automated **Nmap security scans**  
+  - **Quantum-readiness checks**  
+  - **Network Monitoring tasks**  
+
+🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  
+- ✅ **Zero-configuration setup**  
+- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
+- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  
+
+### **Other Assistants**  
+🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
+- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. 
+- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
+- **Real-time network diagnostics and monitoring**
+- **Security Audits**
+- **Penetration testing** (Nmap/Metasploit)  
+
+🔵 **HugLLM** – Latest Open-source models:  
+- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
+
+### 💡 **Example commands you could test**:  
+1. `"Give me info on my websites SSL certificate"`  
+2. `"Check if my server is using quantum safe encyption for communication"`  
+3. `"Run a comprehensive security audit on my server"`
+4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!
+
+### Final Word
+
+I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.
+
+If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
+
+I'm also open to job opportunities or sponsorship.
+
+Thank you! 😊