chore: sync models with dashboard API

README.md

@@ -21,7 +21,6 @@ The official command-line interface for [ZeroGPU](https://zerogpu.ai) — run fa
     - [`chat`](#chat)
     - [`chat_thinking`](#chat_thinking)
     - [`summarize`](#summarize)
-    - [`generate_followups`](#generate_followups)
   - [Classification](#classification)
     - [`classify_iab`](#classify_iab)
     - [`classify_iab_enriched`](#classify_iab_enriched)
@@ -140,11 +139,11 @@ zerogpu chat "Why does my API keep getting rate-limited?" -m gpt-oss-120b -r
 # Multilingual reasoning
 zerogpu chat "Explique la mise en cache en une phrase." -m qwen3-30b-a3b-fp8
 
-# A 1M-token context, for whole repos and very long documents
+# A 262K-token context, for whole repos and very long documents
 zerogpu chat "$(cat ARCHITECTURE.md)" -m glm-5.2
 
 # Coding and agentic work, at a fraction of the flagship price
-zerogpu chat "Port this helper to async/await." -m deepseek-v4-flash
+zerogpu chat "Port this helper to async/await." -m deepseek-v4-flash-0731
 ```
 
 | Option | Description |
@@ -157,12 +156,13 @@ zerogpu chat "Port this helper to async/await." -m deepseek-v4-flash
 |---|---|
 | `LFM2.5-1.2B-Instruct` | Default. Fast edge chat. |
 | `LFM2.5-1.2B-Thinking` | Compact reasoning model. |
-| `gpt-oss-120b` | 117B MoE, 131K context, reasoning + function calling. |
-| `qwen3-30b-a3b-fp8` | 30.5B MoE, 100+ languages, reasoning + function calling. |
-| `glm-5.2` | 753B MoE, 1M context, reasoning + function calling. The platform's most capable model, and its priciest. |
-| `deepseek-v4-flash` | 284B MoE (13B active), 1M context, coding and agentic workflows. |
+| `gpt-oss-120b` | 120B MoE, 131K context, reasoning + function calling. |
+| `llama-guard-4-12b` | 12B dense, 164K context, brand safety + text moderation. |
+| `qwen3-30b-a3b-fp8` | 30B MoE, 100+ languages, reasoning + function calling. |
+| `glm-5.2` | 753B MoE, 262K context, reasoning + function calling. The platform's most capable model, and its priciest. |
+| `deepseek-v4-flash-0731` | 284B MoE (13B active), 1M context, coding and agentic workflows. |
 
-`qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash` are served by the Chat Completions API rather than the Responses API; the CLI routes them automatically.
+`qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash-0731` are served by the Chat Completions API rather than the Responses API; the CLI routes them automatically.
 
 #### `chat_thinking`
 
@@ -180,14 +180,6 @@ Summarize text with the **llama-3.1-8b-instruct-fast** model.
 zerogpu summarize "Long article text goes here..."
 ```
 
-#### `generate_followups`
-
-Generate contextual follow-up questions using the ZeroGPU follow-up edge model.
-
-```bash
-zerogpu generate_followups "We just shipped a new pricing page focused on enterprise plans."
-```
-
 ---
 
 ### Classification

docs/ADDING_COMMANDS.md

@@ -9,7 +9,7 @@ This guide explains how to add a new CLI command to the ZeroGPU CLI.
 - `src/commands/` — one file per command, each exporting a `register<Name>Command(program)` function.
 - `src/cli.ts` — wires every command into the root program.
 - `src/lib/responses.ts` — shared `RESPONSES_ENDPOINT`, `ResponsesApiResponse`, and the `extractOutputText` / `extractReasoningText` helpers for `/v1/responses` calls.
-- `src/lib/chatCompletions.ts` — the same for `/v1/chat/completions`, used by models the platform serves only there (currently `qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash`), plus `toResponsesUsage` to normalize token counts for savings tracking.
+- `src/lib/chatCompletions.ts` — the same for `/v1/chat/completions`, used by models the platform serves only there (currently `qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash-0731`), plus `toResponsesUsage` to normalize token counts for savings tracking.
 - `src/lib/auth.ts` — `getApiKey()` for authenticated requests.
 - `src/lib/request.ts` — plumbing for the endpoint commands: `requireApiKey`, `resolveInput` (argument or stdin), `parseJsonObject`, `postJson`, and the print helpers.
 

docs/DOCUMENTATION.md

@@ -5,15 +5,14 @@
 `zerogpu-cli` is the official command-line interface for [ZeroGPU](https://zerogpu.ai), a distributed / edge inference platform for small language models (SLMs) and nano language models. The CLI is a thin, OpenAI-compatible client around the ZeroGPU **Responses API** (`https://api.zerogpu.ai/v1/responses`) — and, for models served only there, the **Chat Completions API** (`https://api.zerogpu.ai/v1/chat/completions`) — that lets you call a curated set of edge-optimized models directly from your terminal for common NLP workloads:
 
 - Conversational chat (`LFM2.5-1.2B-Instruct`, `LFM2.5-1.2B-Thinking`)
-- Reasoning and tool-use chat (`gpt-oss-120b`, `qwen3-30b-a3b-fp8`, `glm-5.2`, `deepseek-v4-flash`)
+- Reasoning and tool-use chat (`gpt-oss-120b`, `llama-guard-4-12b`, `qwen3-30b-a3b-fp8`, `glm-5.2`, `deepseek-v4-flash-0731`)
 - IAB content/audience classification (`zlm-v1-iab-classify-edge`, `zlm-v2-iab-classify-edge-enriched`)
 - Domain-level IAB classification (`zlm-v1-iab-domain-classifier`)
 - Zero-shot classification (`deberta-v3-small`)
 - Structured / schema-driven classification and JSON extraction (`gliner2-base-v1`)
 - Named-entity recognition with custom labels (`gliner2-base-v1`)
 - PII extraction & redaction (`gliner-multi-pii-v1`)
 - Text summarization (`llama-3.1-8b-instruct-fast`)
-- Follow-up question generation (`zlm-v1-followup-questions-edge`)
 
 It is written in TypeScript (ESM, Node ≥ 20), built on [`commander`](https://github.com/tj/commander.js), and ships a single executable: `zerogpu`.
 
@@ -102,14 +101,13 @@ The CLI exposes the following commands:
 | [`redact_pii`](#411-redact_pii) | Mask PII in-line in the text |
 | [`extract_json`](#412-extract_json) | Schema-driven structured JSON extraction |
 | [`summarize`](#413-summarize) | Summarize text with `llama-3.1-8b-instruct-fast` |
-| [`generate_followups`](#414-generate_followups) | Generate follow-up questions |
-| [`classify_domain`](#415-classify_domain) | Domain-level IAB classification |
-| [`responses`](#416-responses) | Call `/v1/responses` with any model |
-| [`chat_completions`](#417-chat_completions) | Call `/v1/chat/completions` with any model |
-| [`moderations`](#418-moderations) | Call `/v1/moderations` with any model |
-| [`embeddings`](#419-embeddings) | Call `/v1/embeddings` with any model |
+| [`classify_domain`](#414-classify_domain) | Domain-level IAB classification |
+| [`responses`](#415-responses) | Call `/v1/responses` with any model |
+| [`chat_completions`](#416-chat_completions) | Call `/v1/chat/completions` with any model |
+| [`moderations`](#417-moderations) | Call `/v1/moderations` with any model |
+| [`embeddings`](#418-embeddings) | Call `/v1/embeddings` with any model |
 
-Commands 4.1–4.15 each wrap one task and pin its model. The endpoint commands, 4.16–4.19, take the model from `--model` and keep no model list, so a model the platform adds or renames works with them without a CLI release.
+Commands 4.1–4.14 each wrap one task and pin its model. The endpoint commands, 4.15–4.18, take the model from `--model` and keep no model list, so a model the platform adds or renames works with them without a CLI release.
 
 ### Common exit codes
 | Code | Meaning |
@@ -213,12 +211,13 @@ zerogpu chat <text> [-i <instructions>] [-m <model>] [-r]
 |---|---|---|
 | `LFM2.5-1.2B-Instruct` | Responses | Default. Fast edge chat. |
 | `LFM2.5-1.2B-Thinking` | Responses | Compact reasoning model. |
-| `gpt-oss-120b` | Responses | 117B MoE, 131K context, reasoning + function calling. |
-| `qwen3-30b-a3b-fp8` | Chat Completions | 30.5B MoE, 100+ languages, reasoning + function calling. |
-| `glm-5.2` | Chat Completions | 753B MoE, 1,048,576-token context, reasoning + function calling. The most capable model on the platform, and the most expensive by an order of magnitude. |
-| `deepseek-v4-flash` | Chat Completions | 284B MoE (13B active), 1,048,576-token context, coding and agentic workflows. |
+| `gpt-oss-120b` | Responses | 120B MoE, 131K context, reasoning + function calling. |
+| `llama-guard-4-12b` | Responses | 12B dense, 163,840-token context, brand safety + text moderation. |
+| `qwen3-30b-a3b-fp8` | Chat Completions | 30B MoE, 100+ languages, reasoning + function calling. |
+| `glm-5.2` | Chat Completions | 753B MoE, 262,144-token context, reasoning + function calling. The most capable model on the platform, and the most expensive by an order of magnitude. |
+| `deepseek-v4-flash-0731` | Chat Completions | 284B MoE (13B active), 1,048,576-token context, coding and agentic workflows. |
 
-`qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash` have no Responses endpoint, so the CLI posts them to `/v1/chat/completions` instead, mapping `--instructions` to a `system` message and normalizing `prompt_tokens` / `completion_tokens` back to Responses token names for savings tracking. This routing is transparent — the command and its output are identical either way.
+`qwen3-30b-a3b-fp8`, `glm-5.2`, and `deepseek-v4-flash-0731` have no Responses endpoint, so the CLI posts them to `/v1/chat/completions` instead, mapping `--instructions` to a `system` message and normalizing `prompt_tokens` / `completion_tokens` back to Responses token names for savings tracking. This routing is transparent — the command and its output are identical either way.
 
 **Example**
 ```bash
@@ -605,41 +604,7 @@ A single condensed summary string.
 
 ---
 
-### 4.14 `generate_followups`
-
-Generate contextual follow-up questions using `zlm-v1-followup-questions-edge`.
-
-**Synopsis**
-```
-zerogpu generate_followups <text>
-```
-
-**Parameters**
-
-| Name | Type | Required | Description |
-|---|---|---|---|
-| `text` (positional) | string | yes | Conversation turn / passage to generate follow-ups for. |
-
-**Example**
-```bash
-zerogpu generate_followups \
-  "Solar panel adoption increased 35% in the US last year."
-```
-
-**Expected output (illustrative)**
-```json
-[
-  "Which states drove the largest share of the increase?",
-  "How does residential adoption compare to commercial?",
-  "What policy changes contributed to this growth?"
-]
-```
-
-**Outcomes** — same as the common table.
-
----
-
-### 4.15 `classify_domain`
+### 4.14 `classify_domain`
 
 Classify a domain name against the IAB taxonomy using `zlm-v1-iab-domain-classifier`. It infers the categories that characterize a site as a whole from the hostname alone — no crawl, no page text — which keeps payloads roughly 10x smaller than page-level classification. Use it for bidstream enrichment, allow/deny-list scoring, and inventory-level targeting; when you have the page text and need per-URL precision, use [`classify_iab`](#45-classify_iab) instead.
 
@@ -680,7 +645,7 @@ zerogpu classify_domain nytimes.com
 
 ---
 
-### 4.16 `responses`
+### 4.15 `responses`
 
 Call the Responses API with any model. The model id is sent exactly as given — the CLI does not check it against a list, so the API decides whether it exists.
 
@@ -734,7 +699,7 @@ Email [PERSON] at [EMAIL] about invoice 12345.
 
 ---
 
-### 4.17 `chat_completions`
+### 4.16 `chat_completions`
 
 Call the Chat Completions API with any model. Alias: `chat-completions`.
 
@@ -777,11 +742,11 @@ zerogpu chat_completions "The application is built with Python 3.11 and uses Pos
 }
 ```
 
-**Outcomes** — as for [`responses`](#416-responses), except a response with no message content exits `1` with `Response did not contain any message content.` + raw JSON dump.
+**Outcomes** — as for [`responses`](#415-responses), except a response with no message content exits `1` with `Response did not contain any message content.` + raw JSON dump.
 
 ---
 
-### 4.18 `moderations`
+### 4.17 `moderations`
 
 Call the Moderations API with any model and print the response.
 
@@ -805,11 +770,11 @@ zerogpu moderations "Screen this comment before we publish it." -m zlm-v1-modera
 
 **Expected output** — the moderations response as JSON: `results[].flagged`, `results[].categories`, and `results[].category_scores`.
 
-**Outcomes** — as for [`responses`](#416-responses); there is no content to extract, so a successful response is always printed.
+**Outcomes** — as for [`responses`](#415-responses); there is no content to extract, so a successful response is always printed.
 
 ---
 
-### 4.19 `embeddings`
+### 4.18 `embeddings`
 
 Call the Embeddings API with any model and print the response.
 
@@ -833,13 +798,13 @@ zerogpu embeddings "ZeroGPU runs small models at the edge." -m all-minilm-l6-v2
 
 **Expected output** — the embeddings response as JSON: `data[].embedding` holds the vector and `usage` the input tokens.
 
-**Outcomes** — as for [`moderations`](#418-moderations).
+**Outcomes** — as for [`moderations`](#417-moderations).
 
 ---
 
 ## 5. Network & API Contract
 
-The endpoint commands (4.16–4.19) POST to the endpoint they are named for — `/v1/responses`, `/v1/chat/completions`, `/v1/moderations`, `/v1/embeddings` — with the headers below. The rest of this section describes the task commands.
+The endpoint commands (4.15–4.18) POST to the endpoint they are named for — `/v1/responses`, `/v1/chat/completions`, `/v1/moderations`, `/v1/embeddings` — with the headers below. The rest of this section describes the task commands.
 
 All inference commands POST to:
 
@@ -849,7 +814,7 @@ Content-Type: application/json
 x-api-key:    <ZEROGPU_API_KEY>
 ```
 
-The exceptions are `summarize`, `chat --model qwen3-30b-a3b-fp8`, `chat --model glm-5.2`, and `chat --model deepseek-v4-flash`, whose models the ZeroGPU platform serves only through the OpenAI-compatible Chat Completions endpoint:
+The exceptions are `summarize`, `chat --model qwen3-30b-a3b-fp8`, `chat --model glm-5.2`, and `chat --model deepseek-v4-flash-0731`, whose models the ZeroGPU platform serves only through the OpenAI-compatible Chat Completions endpoint:
 
 ```
 POST https://api.zerogpu.ai/v1/chat/completions

package.json

@@ -1,6 +1,6 @@
 {
   "name": "zerogpu-cli",
-  "version": "3.8.0",
+  "version": "3.9.0",
   "description": "Command-line interface for ZeroGPU.",
   "type": "module",
   "bin": {

src/cli.ts

@@ -16,7 +16,6 @@ import { registerEmbeddingsCommand } from "./commands/embeddings.js";
 import { registerExtractEntitiesCommand } from "./commands/extractEntities.js";
 import { registerExtractJsonCommand } from "./commands/extractJson.js";
 import { registerExtractPiiCommand } from "./commands/extractPii.js";
-import { registerGenerateFollowupsCommand } from "./commands/generateFollowups.js";
 import { registerLoginCommand } from "./commands/login.js";
 import { registerModerationsCommand } from "./commands/moderations.js";
 import { registerRedactPiiCommand } from "./commands/redactPii.js";
@@ -103,7 +102,6 @@ export function buildProgram(): Command {
   registerClassifyDomainCommand(program);
   registerClassifyStructuredCommand(program);
   registerClassifyZeroShotCommand(program);
-  registerGenerateFollowupsCommand(program);
   registerRedactPiiCommand(program);
   registerExtractPiiCommand(program);
   registerExtractEntitiesCommand(program);

src/commands/chat.ts

@@ -17,16 +17,17 @@ import { recordAndMaybeNotify } from "../lib/savings.js";
 const DEFAULT_MODEL = "LFM2.5-1.2B-Instruct";
 
 // Text-generation models `--model` accepts, and the API each one speaks.
-// qwen3-30b-a3b-fp8, glm-5.2, and deepseek-v4-flash are Chat Completions only —
-// they have no Responses endpoint.
+// qwen3-30b-a3b-fp8, glm-5.2, and deepseek-v4-flash-0731 are Chat Completions
+// only — they have no Responses endpoint.
 // Source: https://docs.zerogpu.ai/docs/text-generation
 const CHAT_MODELS: Record<string, "responses" | "chat-completions"> = {
   "LFM2.5-1.2B-Instruct": "responses",
   "LFM2.5-1.2B-Thinking": "responses",
   "gpt-oss-120b": "responses",
+  "llama-guard-4-12b": "responses",
   "qwen3-30b-a3b-fp8": "chat-completions",
   "glm-5.2": "chat-completions",
-  "deepseek-v4-flash": "chat-completions",
+  "deepseek-v4-flash-0731": "chat-completions",
 };
 
 // Model ids are case-sensitive to the API but not to the person typing them.