docs: specialist card with usage, examples and family index

README.md

@@ -1,47 +1,61 @@
 ---
 library_name: peft
 base_model: meta-llama/Llama-3.1-8B-Instruct
-license: cc-by-nc-4.0
 pipeline_tag: text-generation
+language:
+- en
+license: cc-by-nc-4.0
 tags:
-  - lora
-  - peft
-  - molly-os
-  - security
+- lora
+- peft
+- molly-os
+- specialist
+- security-audit
+- llama-3.1
+- domain-adaptation
 ---
 
-# Molly OS - Specialist Adapter: Security Audit
+# Molly Specialist — Security Audit
+
+Identifies vulnerabilities in source code, configurations, and infrastructure-as-code with CWE-mapped findings and remediation steps exceeding base model specificity.
+
+Part of **[Molly](https://iamolly.ai/?utm_source=huggingface&utm_medium=model_card&utm_campaign=specialists&utm_content=molly-security-audit)**, an orchestrator that keeps a library of small domain
+specialists over one quantized base and routes each request to the right one, so a
+single machine answers across many fields without loading a separate large model
+for each.
+
+## What this specialist handles well
 
-Frontier-distilled **LoRA specialist** (PEFT, rank 32; target modules
-`q_proj`, `k_proj`, `v_proj`, `o_proj`) for the Molly OS model-agnostic
-orchestration layer. Base model: **[meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)**.
-Domain: **Security Audit**.
+- Maps code vulnerabilities to specific CWE IDs with severity ratings
+- Generates compliance gap analyses for SOC 2 and ISO 27001
+- Reviews IAM policies for privilege escalation and over-permissive access
 
-Adapter weights are released under **CC BY-NC 4.0**. The base model is governed by
-its own (Llama 3.1) license.
+## Try it with
+
+- "Audit this Terraform configuration for misconfigurations and security best practices violations"
+- "What CWE categories apply to this SQL injection vulnerability in the login handler?"
+- "Generate a SOC 2 access control checklist for a multi-tenant SaaS application"
 
 ## Before you run: the base model is gated
 
 This adapter needs the base weights, and the base is **access-gated**. Do this **once**:
 
-1. Open the base page and **accept its license**: <https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct>
+1. Accept the base licence: <https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct>
 2. Create a **read token**: <https://huggingface.co/settings/tokens>
-3. Make the token available to your environment:
-   - **Google Colab:** open the **Secrets** panel (key icon, left sidebar) -> *Add new secret* -> Name `HF_TOKEN`, paste the value, enable **Notebook access**.
-   - **Kaggle:** *Add-ons -> Secrets* -> add `HF_TOKEN`.
-   - **Local:** run `huggingface-cli login` or `export HF_TOKEN=...`.
+3. Make the token available:
+   - **Google Colab:** Secrets panel (key icon) → *Add new secret* → name `HF_TOKEN`, enable **Notebook access**.
+   - **Kaggle:** *Add-ons → Secrets* → add `HF_TOKEN`.
+   - **Local:** `huggingface-cli login` or `export HF_TOKEN=...`
 
-If you skip this you will get `GatedRepoError` / `401 Unauthorized` when the **base** loads.
-A stored Colab secret is **not** used automatically - you must authenticate in code (see below).
+Skipping this gives `GatedRepoError` / `401 Unauthorized` when the **base** loads. A stored
+Colab secret is **not** applied automatically — authenticate in code, as below.
 
 ## Quickstart
 
 ```python
 # pip install -U transformers peft accelerate
-import os
+import os, torch
 from huggingface_hub import login
-
-# Authenticate (Colab secret -> env var -> interactive prompt)
 try:
     from google.colab import userdata
     login(userdata.get("HF_TOKEN"))
@@ -49,7 +63,6 @@ except Exception:
     tok = os.environ.get("HF_TOKEN")
     login(tok) if tok else login()
 
-import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
 
@@ -66,9 +79,7 @@ out = model.generate(ids, max_new_tokens=300)
 print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
 ```
 
-## Low-VRAM (4-bit) - fits a free Colab/Kaggle GPU (~6-7 GB)
-
-Use a **GPU runtime** (Colab: *Runtime -> Change runtime type -> T4 GPU*).
+## Low-VRAM (4-bit) — fits a free Colab/Kaggle GPU (~6–7 GB)
 
 ```python
 # pip install -U transformers peft accelerate bitsandbytes
@@ -83,26 +94,50 @@ except Exception:
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 from peft import PeftModel
 
-BASE = "meta-llama/Llama-3.1-8B-Instruct"
-ADAPTER = "BoomJules/molly-security-audit"
-
 bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                          bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
-tok = AutoTokenizer.from_pretrained(BASE)
-base = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")
-model = PeftModel.from_pretrained(base, ADAPTER).eval()
+tok = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
+base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", quantization_config=bnb, device_map="auto")
+model = PeftModel.from_pretrained(base, "BoomJules/molly-security-audit").eval()
 ```
 
+## Adapter details
+
+| | |
+|---|---|
+| Base model | `meta-llama/Llama-3.1-8B-Instruct` |
+| Method | LoRA (PEFT) |
+| Rank / alpha | 32 / 64 |
+| Domain | Security Audit |
+
 ## Troubleshooting
 
-- **`GatedRepoError` / `401 Unauthorized`** - base license not accepted, or `HF_TOKEN`
-  missing/invalid, or you stored the Colab secret but did not call `login(...)` in code.
-- **CUDA out of memory** - use the 4-bit snippet and a GPU runtime.
-- **Adapter seems to have no effect** - confirm the base id matches `base_model` above.
+- **`GatedRepoError` / `401 Unauthorized`** — base licence not accepted, or `HF_TOKEN` missing,
+  or the Colab secret was stored but `login(...)` was never called.
+- **CUDA out of memory** — use the 4-bit snippet on a GPU runtime.
+- **Adapter seems to have no effect** — confirm the base id matches `base_model` above.
+
+## Other Molly specialists
+
+- [Quantum Software Architect](https://huggingface.co/BoomJules/molly-quantum-software-architect)
+- [Quantum Communication Systems Engineer](https://huggingface.co/BoomJules/molly-quantum-communication-systems-engineer)
+- [Infectious Disease Physician Antimicrobial Stewardship](https://huggingface.co/BoomJules/molly-infectious-disease-physician-antimicrobial-stewardship)
+- [Health Informatics Medical AI Specialist](https://huggingface.co/BoomJules/molly-health-informatics-medical-ai-specialist)
+- [Clinical Trial Pharmacologist](https://huggingface.co/BoomJules/molly-clinical-trial-pharmacologist)
+- [Immunopharmacologist](https://huggingface.co/BoomJules/molly-immunopharmacologist)
+- [Climate Analytics Manager](https://huggingface.co/BoomJules/molly-climate-analytics-manager)
+- [Language Technology Consultant](https://huggingface.co/BoomJules/molly-language-technology-consultant)
+- [Polymer Chemist](https://huggingface.co/BoomJules/molly-polymer-chemist)
+- [Composite Materials Engineer](https://huggingface.co/BoomJules/molly-composite-materials-engineer)
+- [Computer Science AI](https://huggingface.co/BoomJules/molly-cs-ai)
+- [Computer Science Algorithms](https://huggingface.co/BoomJules/molly-cs-algorithms)
+
+Running several of these at once, with the routing decided for you, is what
+[Molly](https://iamolly.ai/?utm_source=huggingface&utm_medium=model_card&utm_campaign=specialists&utm_content=molly-security-audit) does.
 
-## License & intended use
+## Licence & intended use
 
-Adapter: **CC BY-NC 4.0** (attribution, non-commercial). Base model: Llama 3.1 license.
+Adapter: **CC BY-NC 4.0** (attribution, non-commercial). Base model: its own licence.
 Intended for research and evaluation in Security Audit.
 
-(c) 2026 Core Labs R&D.
+© 2026 Core Labs R&D.